longitudinal_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datacomp as dc\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load Data\n",
    "datacol = dc.get_data(\"data/simulated.csv\", [\"Dataset1\", \"Dataset2\", \"Dataset3\"], [\"CATSIG1\", \"CATNON2\"], \"DATASET\")\n",
    "\n",
    "# exlude following features from comparison\n",
    "exclude_feats = [\"DATASET\",  \"ENTITY\"]\n",
    "\n",
    "# feature subset of interest\n",
    "feats = [\"NDSIG1\", \"LONG1\", \"FEAT1\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Number Of Entities Per Time Point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.5/dist-packages/seaborn/categorical.py:1468: FutureWarning: remove_na is deprecated and is a private function. Do not use.\n",
      "  stat_data = remove_na(group_data[hue_mask])\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGNCAYAAABJ++jzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlJsFhSRJys2CQpIk5WZBIUmScvv/mLS9oc0y8akAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0ffa669b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_entities_per_timepoint(datacol, \"TIME\", \"DATASET\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Longitudinal Analysis w.o. Normalization\n",
    "\n",
    "In this section the longitudinal comparison is applied to unnormalized raw data values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>p-value</th>\n",
       "      <th>cor_p-value</th>\n",
       "      <th>signf</th>\n",
       "      <th>Dataset1conf.</th>\n",
       "      <th>Dataset2conf.</th>\n",
       "      <th>Dataset3conf.</th>\n",
       "      <th>Dataset1</th>\n",
       "      <th>Dataset2</th>\n",
       "      <th>Dataset3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>features</th>\n",
       "      <th></th>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">FEAT1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.890923e-01</td>\n",
       "      <td>9.480179e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[439.45, 496.35]</td>\n",
       "      <td>[445.03, 499.32]</td>\n",
       "      <td>[445.46, 500.23]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>6.574574e-01</td>\n",
       "      <td>9.918561e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[439.45, 496.35]</td>\n",
       "      <td>[445.03, 499.32]</td>\n",
       "      <td>[445.46, 500.23]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>6.179089e-01</td>\n",
       "      <td>9.918561e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[439.45, 496.35]</td>\n",
       "      <td>[445.03, 499.32]</td>\n",
       "      <td>[445.46, 500.23]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.218083e-01</td>\n",
       "      <td>9.352958e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[402.57, 452.78]</td>\n",
       "      <td>[389.69, 449.71]</td>\n",
       "      <td>[402.08, 451.01]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>5.985382e-01</td>\n",
       "      <td>9.352958e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[402.57, 452.78]</td>\n",
       "      <td>[389.69, 449.71]</td>\n",
       "      <td>[402.08, 451.01]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>8.331970e-01</td>\n",
       "      <td>9.352958e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[402.57, 452.78]</td>\n",
       "      <td>[389.69, 449.71]</td>\n",
       "      <td>[402.08, 451.01]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>4.963787e-02</td>\n",
       "      <td>1.416442e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[354.41, 418.99]</td>\n",
       "      <td>[394.19, 461.26]</td>\n",
       "      <td>[397.65, 456.1]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>5.951061e-02</td>\n",
       "      <td>1.416442e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[354.41, 418.99]</td>\n",
       "      <td>[394.19, 461.26]</td>\n",
       "      <td>[397.65, 456.1]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>7.689406e-01</td>\n",
       "      <td>7.689406e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[354.41, 418.99]</td>\n",
       "      <td>[394.19, 461.26]</td>\n",
       "      <td>[397.65, 456.1]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>6.693517e-04</td>\n",
       "      <td>2.006711e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[314.16, 369.99]</td>\n",
       "      <td>[389.05, 457.05]</td>\n",
       "      <td>[428.46, 494.7]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>2.152309e-06</td>\n",
       "      <td>1.076150e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>[314.16, 369.99]</td>\n",
       "      <td>[389.05, 457.05]</td>\n",
       "      <td>[428.46, 494.7]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.233656e-01</td>\n",
       "      <td>1.233656e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[314.16, 369.99]</td>\n",
       "      <td>[389.05, 457.05]</td>\n",
       "      <td>[428.46, 494.7]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.858769e-06</td>\n",
       "      <td>9.293812e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>[275.91, 334.29]</td>\n",
       "      <td>[384.93, 443.07]</td>\n",
       "      <td>[459.5, 512.26]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.216892e-11</td>\n",
       "      <td>7.373515e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>[275.91, 334.29]</td>\n",
       "      <td>[384.93, 443.07]</td>\n",
       "      <td>[459.5, 512.26]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.331053e-03</td>\n",
       "      <td>2.660333e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[275.91, 334.29]</td>\n",
       "      <td>[384.93, 443.07]</td>\n",
       "      <td>[459.5, 512.26]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">LONG1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.913031e-01</td>\n",
       "      <td>9.918561e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[11.32, 12.27]</td>\n",
       "      <td>[11.22, 12.12]</td>\n",
       "      <td>[11.35, 12.27]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>7.859661e-01</td>\n",
       "      <td>9.918561e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[11.32, 12.27]</td>\n",
       "      <td>[11.22, 12.12]</td>\n",
       "      <td>[11.35, 12.27]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>7.605155e-01</td>\n",
       "      <td>9.918561e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[11.32, 12.27]</td>\n",
       "      <td>[11.22, 12.12]</td>\n",
       "      <td>[11.35, 12.27]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.505636e-05</td>\n",
       "      <td>3.752255e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>[14.42, 15.02]</td>\n",
       "      <td>[15.35, 16.01]</td>\n",
       "      <td>[13.24, 14.03]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.343695e-05</td>\n",
       "      <td>3.752255e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>[14.42, 15.02]</td>\n",
       "      <td>[15.35, 16.01]</td>\n",
       "      <td>[13.24, 14.03]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>2.740259e-09</td>\n",
       "      <td>2.192207e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[14.42, 15.02]</td>\n",
       "      <td>[15.35, 16.01]</td>\n",
       "      <td>[13.24, 14.03]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.691277e-06</td>\n",
       "      <td>1.014762e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>[16.58, 17.45]</td>\n",
       "      <td>[18.42, 19.53]</td>\n",
       "      <td>[15.7, 16.61]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.964105e-02</td>\n",
       "      <td>7.627974e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>[16.58, 17.45]</td>\n",
       "      <td>[18.42, 19.53]</td>\n",
       "      <td>[15.7, 16.61]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>4.104054e-09</td>\n",
       "      <td>3.283243e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[16.58, 17.45]</td>\n",
       "      <td>[18.42, 19.53]</td>\n",
       "      <td>[15.7, 16.61]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.352469e-13</td>\n",
       "      <td>3.017586e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>[18.64, 19.93]</td>\n",
       "      <td>[22.9, 23.74]</td>\n",
       "      <td>[17.46, 18.79]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>4.958191e-03</td>\n",
       "      <td>9.891798e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[18.64, 19.93]</td>\n",
       "      <td>[22.9, 23.74]</td>\n",
       "      <td>[17.46, 18.79]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.158386e-12</td>\n",
       "      <td>9.267254e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>[18.64, 19.93]</td>\n",
       "      <td>[22.9, 23.74]</td>\n",
       "      <td>[17.46, 18.79]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.693678e-08</td>\n",
       "      <td>5.385573e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>[21.15, 22.26]</td>\n",
       "      <td>[18.46, 19.69]</td>\n",
       "      <td>[19.45, 20.78]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>6.808472e-04</td>\n",
       "      <td>2.041151e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[21.15, 22.26]</td>\n",
       "      <td>[18.46, 19.69]</td>\n",
       "      <td>[19.45, 20.78]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>3.197597e-02</td>\n",
       "      <td>3.197597e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>[21.15, 22.26]</td>\n",
       "      <td>[18.46, 19.69]</td>\n",
       "      <td>[19.45, 20.78]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">NDSIG1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>2.721885e-02</td>\n",
       "      <td>1.756607e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[9.3, 11.1]</td>\n",
       "      <td>[10.71, 12.88]</td>\n",
       "      <td>[15.16, 16.43]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>4.100790e-11</td>\n",
       "      <td>3.690708e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>[9.3, 11.1]</td>\n",
       "      <td>[10.71, 12.88]</td>\n",
       "      <td>[15.16, 16.43]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>2.199128e-07</td>\n",
       "      <td>1.759301e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>[9.3, 11.1]</td>\n",
       "      <td>[10.71, 12.88]</td>\n",
       "      <td>[15.16, 16.43]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.042609e-06</td>\n",
       "      <td>6.255639e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>[7.54, 9.67]</td>\n",
       "      <td>[11.66, 13.53]</td>\n",
       "      <td>[15.67, 16.97]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.098751e-13</td>\n",
       "      <td>8.188450e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>[7.54, 9.67]</td>\n",
       "      <td>[11.66, 13.53]</td>\n",
       "      <td>[15.67, 16.97]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>7.400164e-08</td>\n",
       "      <td>5.180114e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>[7.54, 9.67]</td>\n",
       "      <td>[11.66, 13.53]</td>\n",
       "      <td>[15.67, 16.97]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.435581e-05</td>\n",
       "      <td>1.717672e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>[8.8, 10.62]</td>\n",
       "      <td>[11.62, 13.63]</td>\n",
       "      <td>[15.82, 16.88]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.158386e-12</td>\n",
       "      <td>1.042566e-11</td>\n",
       "      <td>True</td>\n",
       "      <td>[8.8, 10.62]</td>\n",
       "      <td>[11.62, 13.63]</td>\n",
       "      <td>[15.82, 16.88]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>2.395774e-08</td>\n",
       "      <td>1.677042e-07</td>\n",
       "      <td>True</td>\n",
       "      <td>[8.8, 10.62]</td>\n",
       "      <td>[11.62, 13.63]</td>\n",
       "      <td>[15.82, 16.88]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.138062e-04</td>\n",
       "      <td>1.254634e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[8.63, 10.44]</td>\n",
       "      <td>[11.08, 12.85]</td>\n",
       "      <td>[15.55, 16.93]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>6.584223e-12</td>\n",
       "      <td>4.608924e-11</td>\n",
       "      <td>True</td>\n",
       "      <td>[8.63, 10.44]</td>\n",
       "      <td>[11.08, 12.85]</td>\n",
       "      <td>[15.55, 16.93]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>3.588785e-09</td>\n",
       "      <td>2.153271e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[8.63, 10.44]</td>\n",
       "      <td>[11.08, 12.85]</td>\n",
       "      <td>[15.55, 16.93]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.505487e-04</td>\n",
       "      <td>1.401458e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[9.39, 10.9]</td>\n",
       "      <td>[11.55, 13.36]</td>\n",
       "      <td>[15.29, 16.65]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.359896e-12</td>\n",
       "      <td>1.223921e-11</td>\n",
       "      <td>True</td>\n",
       "      <td>[9.39, 10.9]</td>\n",
       "      <td>[11.55, 13.36]</td>\n",
       "      <td>[15.29, 16.65]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.732154e-07</td>\n",
       "      <td>1.039292e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>[9.39, 10.9]</td>\n",
       "      <td>[11.55, 13.36]</td>\n",
       "      <td>[15.29, 16.65]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           p-value   cor_p-value  signf     Dataset1conf.  \\\n",
       "features    datasets                                                        \n",
       "FEAT1    2  (1, 2)    3.890923e-01  9.480179e-01  False  [439.45, 496.35]   \n",
       "            (1, 3)    6.574574e-01  9.918561e-01  False  [439.45, 496.35]   \n",
       "            (2, 3)    6.179089e-01  9.918561e-01  False  [439.45, 496.35]   \n",
       "         4  (1, 2)    7.218083e-01  9.352958e-01  False  [402.57, 452.78]   \n",
       "            (1, 3)    5.985382e-01  9.352958e-01  False  [402.57, 452.78]   \n",
       "            (2, 3)    8.331970e-01  9.352958e-01  False  [402.57, 452.78]   \n",
       "         6  (1, 2)    4.963787e-02  1.416442e-01  False  [354.41, 418.99]   \n",
       "            (1, 3)    5.951061e-02  1.416442e-01  False  [354.41, 418.99]   \n",
       "            (2, 3)    7.689406e-01  7.689406e-01  False  [354.41, 418.99]   \n",
       "         8  (1, 2)    6.693517e-04  2.006711e-03   True  [314.16, 369.99]   \n",
       "            (1, 3)    2.152309e-06  1.076150e-05   True  [314.16, 369.99]   \n",
       "            (2, 3)    1.233656e-01  1.233656e-01  False  [314.16, 369.99]   \n",
       "         10 (1, 2)    1.858769e-06  9.293812e-06   True  [275.91, 334.29]   \n",
       "            (1, 3)    9.216892e-11  7.373515e-10   True  [275.91, 334.29]   \n",
       "            (2, 3)    1.331053e-03  2.660333e-03   True  [275.91, 334.29]   \n",
       "LONG1    2  (1, 2)    7.913031e-01  9.918561e-01  False    [11.32, 12.27]   \n",
       "            (1, 3)    7.859661e-01  9.918561e-01  False    [11.32, 12.27]   \n",
       "            (2, 3)    7.605155e-01  9.918561e-01  False    [11.32, 12.27]   \n",
       "         4  (1, 2)    7.505636e-05  3.752255e-04   True    [14.42, 15.02]   \n",
       "            (1, 3)    9.343695e-05  3.752255e-04   True    [14.42, 15.02]   \n",
       "            (2, 3)    2.740259e-09  2.192207e-08   True    [14.42, 15.02]   \n",
       "         6  (1, 2)    1.691277e-06  1.014762e-05   True    [16.58, 17.45]   \n",
       "            (1, 3)    1.964105e-02  7.627974e-02  False    [16.58, 17.45]   \n",
       "            (2, 3)    4.104054e-09  3.283243e-08   True    [16.58, 17.45]   \n",
       "         8  (1, 2)    3.352469e-13  3.017586e-12   True    [18.64, 19.93]   \n",
       "            (1, 3)    4.958191e-03  9.891798e-03   True    [18.64, 19.93]   \n",
       "            (2, 3)    1.158386e-12  9.267254e-12   True    [18.64, 19.93]   \n",
       "         10 (1, 2)    7.693678e-08  5.385573e-07   True    [21.15, 22.26]   \n",
       "            (1, 3)    6.808472e-04  2.041151e-03   True    [21.15, 22.26]   \n",
       "            (2, 3)    3.197597e-02  3.197597e-02   True    [21.15, 22.26]   \n",
       "NDSIG1   2  (1, 2)    2.721885e-02  1.756607e-01  False       [9.3, 11.1]   \n",
       "            (1, 3)    4.100790e-11  3.690708e-10   True       [9.3, 11.1]   \n",
       "            (2, 3)    2.199128e-07  1.759301e-06   True       [9.3, 11.1]   \n",
       "         4  (1, 2)    1.042609e-06  6.255639e-06   True      [7.54, 9.67]   \n",
       "            (1, 3)    9.098751e-13  8.188450e-12   True      [7.54, 9.67]   \n",
       "            (2, 3)    7.400164e-08  5.180114e-07   True      [7.54, 9.67]   \n",
       "         6  (1, 2)    3.435581e-05  1.717672e-04   True      [8.8, 10.62]   \n",
       "            (1, 3)    1.158386e-12  1.042566e-11   True      [8.8, 10.62]   \n",
       "            (2, 3)    2.395774e-08  1.677042e-07   True      [8.8, 10.62]   \n",
       "         8  (1, 2)    3.138062e-04  1.254634e-03   True     [8.63, 10.44]   \n",
       "            (1, 3)    6.584223e-12  4.608924e-11   True     [8.63, 10.44]   \n",
       "            (2, 3)    3.588785e-09  2.153271e-08   True     [8.63, 10.44]   \n",
       "         10 (1, 2)    3.505487e-04  1.401458e-03   True      [9.39, 10.9]   \n",
       "            (1, 3)    1.359896e-12  1.223921e-11   True      [9.39, 10.9]   \n",
       "            (2, 3)    1.732154e-07  1.039292e-06   True      [9.39, 10.9]   \n",
       "\n",
       "                         Dataset2conf.     Dataset3conf.  Dataset1  Dataset2  \\\n",
       "features    datasets                                                           \n",
       "FEAT1    2  (1, 2)    [445.03, 499.32]  [445.46, 500.23]        40        40   \n",
       "            (1, 3)    [445.03, 499.32]  [445.46, 500.23]        40        40   \n",
       "            (2, 3)    [445.03, 499.32]  [445.46, 500.23]        40        40   \n",
       "         4  (1, 2)    [389.69, 449.71]  [402.08, 451.01]        40        40   \n",
       "            (1, 3)    [389.69, 449.71]  [402.08, 451.01]        40        40   \n",
       "            (2, 3)    [389.69, 449.71]  [402.08, 451.01]        40        40   \n",
       "         6  (1, 2)    [394.19, 461.26]   [397.65, 456.1]        40        40   \n",
       "            (1, 3)    [394.19, 461.26]   [397.65, 456.1]        40        40   \n",
       "            (2, 3)    [394.19, 461.26]   [397.65, 456.1]        40        40   \n",
       "         8  (1, 2)    [389.05, 457.05]   [428.46, 494.7]        40        40   \n",
       "            (1, 3)    [389.05, 457.05]   [428.46, 494.7]        40        40   \n",
       "            (2, 3)    [389.05, 457.05]   [428.46, 494.7]        40        40   \n",
       "         10 (1, 2)    [384.93, 443.07]   [459.5, 512.26]        40        40   \n",
       "            (1, 3)    [384.93, 443.07]   [459.5, 512.26]        40        40   \n",
       "            (2, 3)    [384.93, 443.07]   [459.5, 512.26]        40        40   \n",
       "LONG1    2  (1, 2)      [11.22, 12.12]    [11.35, 12.27]        40        40   \n",
       "            (1, 3)      [11.22, 12.12]    [11.35, 12.27]        40        40   \n",
       "            (2, 3)      [11.22, 12.12]    [11.35, 12.27]        40        40   \n",
       "         4  (1, 2)      [15.35, 16.01]    [13.24, 14.03]        40        40   \n",
       "            (1, 3)      [15.35, 16.01]    [13.24, 14.03]        40        40   \n",
       "            (2, 3)      [15.35, 16.01]    [13.24, 14.03]        40        40   \n",
       "         6  (1, 2)      [18.42, 19.53]     [15.7, 16.61]        40        40   \n",
       "            (1, 3)      [18.42, 19.53]     [15.7, 16.61]        40        40   \n",
       "            (2, 3)      [18.42, 19.53]     [15.7, 16.61]        40        40   \n",
       "         8  (1, 2)       [22.9, 23.74]    [17.46, 18.79]        40        40   \n",
       "            (1, 3)       [22.9, 23.74]    [17.46, 18.79]        40        40   \n",
       "            (2, 3)       [22.9, 23.74]    [17.46, 18.79]        40        40   \n",
       "         10 (1, 2)      [18.46, 19.69]    [19.45, 20.78]        40        40   \n",
       "            (1, 3)      [18.46, 19.69]    [19.45, 20.78]        40        40   \n",
       "            (2, 3)      [18.46, 19.69]    [19.45, 20.78]        40        40   \n",
       "NDSIG1   2  (1, 2)      [10.71, 12.88]    [15.16, 16.43]        40        40   \n",
       "            (1, 3)      [10.71, 12.88]    [15.16, 16.43]        40        40   \n",
       "            (2, 3)      [10.71, 12.88]    [15.16, 16.43]        40        40   \n",
       "         4  (1, 2)      [11.66, 13.53]    [15.67, 16.97]        40        40   \n",
       "            (1, 3)      [11.66, 13.53]    [15.67, 16.97]        40        40   \n",
       "            (2, 3)      [11.66, 13.53]    [15.67, 16.97]        40        40   \n",
       "         6  (1, 2)      [11.62, 13.63]    [15.82, 16.88]        40        40   \n",
       "            (1, 3)      [11.62, 13.63]    [15.82, 16.88]        40        40   \n",
       "            (2, 3)      [11.62, 13.63]    [15.82, 16.88]        40        40   \n",
       "         8  (1, 2)      [11.08, 12.85]    [15.55, 16.93]        40        40   \n",
       "            (1, 3)      [11.08, 12.85]    [15.55, 16.93]        40        40   \n",
       "            (2, 3)      [11.08, 12.85]    [15.55, 16.93]        40        40   \n",
       "         10 (1, 2)      [11.55, 13.36]    [15.29, 16.65]        40        40   \n",
       "            (1, 3)      [11.55, 13.36]    [15.29, 16.65]        40        40   \n",
       "            (2, 3)      [11.55, 13.36]    [15.29, 16.65]        40        40   \n",
       "\n",
       "                      Dataset3  \n",
       "features    datasets            \n",
       "FEAT1    2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "LONG1    2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "NDSIG1   2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result, time_dfs = datacol.analyze_longitudinal_feats(\"TIME\", 0, include=feats)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhwAAAGNCAYAAABaLKgHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCYeIiIiUnBIOERERKTklHCIiIlJy/x+GRpm5GdbigQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd2ee438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGNCAYAAABJ++jzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd1cf518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGNCAYAAABJ++jzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xl4lOW5+PHv7FlmkpksQMIiaOtItEpcioLWrSBWrfVA\n39ZyjkdbrHrcKKjYzSLFHbAuyFZF63Lqe6qV2iqiRYuKLHX5VRoY2SFAkiGTyTr7vL8/3knICpN9\nSO7PdeUKeddnnhlm7rmfzaBpGkIIIYQQ3WHs7wIIIYQQ4vgnAYUQQgghuk0CCiGEEEJ0mwQUQggh\nhOg2CSiEEEII0W0SUAghhBCi2ySgEEIIIUS3SUAhhBBCiG6TgEIIIYQQ3SYBhRBCCCG6zdzfBRBC\n9C+32/3fwErgXo/H82iS51wEzAAmAEOBOFAKvA8s9ng8/27nnOeB64AtQLHH44m1c8wJwG5grsfj\nmdfO/ouBHze7rwWoAD4H/ujxeF45SpmHAC8Al3V0fSFE10lAIYQASGpRH7fbbQV+D/wn+gf/K8A2\nwASclth+k9vt/q3H45nbwT1OBX4GLEi2cG632wY8B1wLeIAXge1AGnByYvuVbrd7GjDd4/EEWp1/\nBfAskJ7sYxVCdI4EFEIIAEOSxz2JHjSsAG71eDzR5jvdbvf9gArc53a7/R6P53etztfQsxj3ud3u\n//V4PAeSvO9i9KDhKeBnHo8n3uq+96EHN98DHkQPWBr3TQbeBP6YOOYvSd5TCNEJ0odCCJEUt9t9\nKvBTYBNwc+tgAsDj8dQBPwAOAvPcbreznUv9D2ADnkjyvqehN3Os93g8d7YOJhL3DaE3p/wcPZPR\nXFqivD8CapO5pxCi8ySgEEIk67/RMwyLPB5Ph80GHo+nFlgKZALT2tn/FbAQuMbtdl+exH2va7zv\n0Q7yeDz1Ho/nUY/H82Wr7X/xeDzLk7iPEKIbJKAQQiTrm4nf/0ji2PfRm1HO62D/b4F9wFOJ/hHJ\n3PejJO4rhOgn0odCCJGsoUDc4/GUJ3FsaeL3sPZ2ejyegNvtngn8GfgF8Jsk7lvReofb7U4HrO2c\nU9feKBIhRO+RDIUQIllxku+82Xhchx/qHo9nFfA34B632/21o1wrdpT7Lgeq2vmZmGQ5hRA9RAIK\nIUSyDgIGt9s9MoljG485eIzjbkcPVJ5J4r4ntLNvPnBRs58XkGGhQvQLCSiEEMlan/j97SSOvRj9\ng/3Dox3k8Xj2AA8Bl7rdbqWDwxr7Tkxu53yPx+NZ1/gD7EmibEKIXiABhRAiWY3f/u9yu92Wjg5y\nu92Z6MNLfeh9JI7lUWAH+iiOrHb2P4+exbgriQ6cQoh+IgGFECIpHo9nF/AYcArwktvtTmt9jNvt\ntqNPbFUAzPJ4PA1JXDcM3AoUoo/+0Frt34c+WdXXADVxjzbcbvel6IEMQKC9Y4QQvUdGeQghGn3D\n7XZP7WDfFx6PZyfwS8COPjnVuW63+2VgK/qXk1OB6UAe+myWLyZ7Y4/H857b7f4/4Pu03wdiLvpk\nWHcB2xP3/RI9czESuAJ9iKoXfertzY0nut3ubwH5iT9PS/w+tdljrfd4PKuTLasQon0SUAghQP8Q\n/1Hipz0/A55MzFJ5u9vtfgm4GVDQsxExYD/wf8BTieCjo/t05GfoC3c5Wu9ITKR1r9vt/t/Efa9E\nz0YY0YOILxPbX2onK3I/8K1WZZjGkUm39gInHqVcQogkGDRNOkQLIYQQontSJkOhKMoZ6NPxno3e\n/vkP4A5VVSsURbkEvSf4Keiz6z2kqmqHyxQLIYQQom+lRIZCURQreqDwJHqnryzgT+i9xG9FX6b4\nNuB/gQvQVwu8QFXVz/qlwEIIIYRoIVUyFBno0+8+r6pqHKhUFOV19CBiOuBRVfWFxLF/VxTlL8AM\n9I5hQgghhOhnKRFQqKrqp9mSw4qiuIHrgT8CZwGtMxGfoXcGE0IIIUQKSImAopGiKKPQmzdM6HP0\n3w+8jd57vDkf+tC0ZOSi9xzfAwR7pKBCCCHE4JAGjAbeASqPdmBKBRSqqu4DbIqinIQeUHQ0jt1A\n8vP1Xwa83APFE0IIIQar6cBRB0OkVEDRSFXVnYqi/BJ97YC/0TYbkYM+9jwZewBqamqIxVJzNWOT\nyURWVlZKlzHVSR12j9Rf90kddp/UYeppfE5IYp2clAgoFEW5GFiiquopzTZriZ/3gB+3OuUcYGOS\nlw8ChEIhIpFId4vaKywWfVmEVC5jqpM67B6pv+6TOuw+qcPU0/ickESXgZQIKIBPgSxFUR5G7zdh\nB34DrENvrrhfUZQfJ/59KXA5ML6fyiqEEEKIVlJicTBVVWuASehBQuM0un7gR6qqHkafZvf2xLaF\nwHRVVf/dT8UVQgghRCupkqEgESBc3MG+j4Divi2REEIIIZKVEhkKIYQQQhzfJKAQQgghRLdJQCGE\nEEKIbpOAQgghhBDdJgGFEEIIIbpNAgohhBBCdJsEFEIIIYToNgkohBBCCNFtElAIIYQQotskoBBC\nCCFEt6XM1NtCCCFS051/3kFZbbjX72MwgMlkJhaLomkt9w1zWHnimq916bqPPPII77zzDgaDAU3T\nMJvN5OTkUFRUxFVXXUVx8cBY2eHQoUPMmzcPj8fDCy+8wMiRI/v0/hJQCCGEOKqy2jD7/aE+ulvv\n3MfpdPLcc8+haRqRSISDBw/y3nvvcddddzFt2jRuueWWpK8ViUS44oorePHFFxk6dGivlPdoVq5c\nSUVFBXPmzGna9o9//IMFCxaQl5eHwWDo8zKBBBRCCCEGAaPRiNPpbPp7yJAhjBs3jjPPPJP58+cz\nZswYpkyZktS1tm3bRiwW662iHlNJSQl5eXktti1ZsoQ77riDWCzGY4891i/lkoBCCCHEoHXJJZfw\n1ltv8corrzBlyhQCgQArVqzgww8/xO/343K5OOecc7j55ptxOBysXr2aRx99FIPBwLXXXsu4ceNY\ntGhRh+fddNNNZGVlAVBXV8eSJUvYtGkT1dXVuFwuzj//fG666SasVisAPp+PpUuXsmXLFiorKyks\nLERRFC6//HIArr32WsrLywFYs2YNixYt4owzzmDRokUUFhayevXq/qlIJKAQQggxyE2cOJGnn34a\nr9fLypUrWb9+Pffddx8jR46krKyMhx9+mIULFzJ37lwuueQS/H4/K1asYOnSpRQWFgLw1FNPtXve\nokWLmDt3LgBPPvkkO3fuZN68eeTl5bFnzx4WLlxILBZj5syZRKNRZs2aRSQSYdasWRQUFLBu3ToW\nLFiA2Wxm0qRJLF26lBtuuIHi4mJuv/12HA4HQFM5+pMEFEIIIQa1IUOGAFBZWcmMGTO47rrrGDZs\nGAD5+flcdNFFvPHGGwBYrVYyMjIAyM7Oxm63AxzzPICvvvqKcePGMXbs2KZjHn/8ceLxOADr1q1j\n//79TVkH0DMSJSUlvPTSS0yaNIns7GwMBgNWq7VFE04qkIBCCCHEoBaNRgEwmUwYDAZee+01Nm3a\nhM/nIxaLEY1GicViRCIRLBZLu9dI5ryJEyfy6quvEg6HmTBhAsXFxRQUFDRdY9u2bZjNZk4//fQW\n1y4uLmb9+vUEg0HS0tJ6ryK6SQIKIYQQg1ppaSmgZypmz56N1+vl1ltv5eSTT8ZqtfLaa6+1yDS0\n5+677z7meTfeeCOjR4/m7bffZt68eQCcd9553HHHHeTm5tLQ0NA0gqS5xg6glZWVDB8+vCcfeo+S\ngEIIIcSgtm7dOtxuNz6fj127djF79mwmT57ctD8cPvocHLt37076vEmTJjFp0iSCwSAbN25kyZIl\nzJ8/n8cffxy73Y7NZuPZZ59Faz0RB/TLENXOkJkyhRBCDFqvv/46O3bsYPr06UQiEUDvG9Govr6e\njz76CKDNh3zj38mcFwqFWLt2LXV1dQCkpaVx4YUXMm3aNLZv3w5AUVER4XCYQCBAYWFh04/NZsNu\nt2MymXqjCnqMBBRCCCEGvHg8js/nw+fzUVlZSUlJCQsWLGDx4sVMnz6diRMnMmrUKBwOB6tWraK0\ntJSSkhLmzJnD+eefD8Dnn39OKBTC4XCgaRqffPIJu3fvPuZ5X3zxBdFolOXLl/PQQw+xbds2vF4v\nW7Zs4d1332XcuHEATJgwgdGjR/PAAw/w6aefUl5ezsaNG7nzzjtZuHBh02NxOBxs376dHTt2UFVV\nRTQabXpsdXV1aJqG3+/H5/NRXV3dZ3VsaC+tMsCcCXzq9XqboshUY7FYyM/PJ5XLmOqkDrtH6q/7\nBnIdDoSpt9esWdNiW1ZWFmPHjmXq1KmcddZZTds3b97M4sWLKS8vp7CwkBkzZlBUVMTMmTMpKytj\n/vz5FBUVce+997J161ZOPPFEli5dyqZNm3jmmWeOet7QoUNZtmwZW7ZsoaGhgZycHMaPH89PfvKT\npuGf1dXVLFu2jA0bNlBbW0tOTg6XXnop119/fdNcFWvWrGHx4sVEIhHmzJlDdnY2s2bNaneGzKFD\nh/LKK690qd7gyOsaOAv47GjHSkCRAgbyG1FfkTrsHqm/7pM67D6pw9TTmYBCmjyEEEII0W0SUAgh\nhBCi2ySgEEIIIUS3SUAhhBBCiG6TgEIIIYQQ3SYBhRBCCCG6TQIKIYQQQnSbBBRCCCGE6DYJKIQQ\nQgjRbRJQCCGEEKLbZPlyIYQQR3XL/ioOReK9fh+DAUw7vcRisTZreRRYjCwZ6erSdR955BHeeecd\nDAYDmqZhNpvJycmhqKiIq666iuLi4h4off8qLy9n+fLlfPnllzQ0NDBy5EiuvfZavvWtb/VZGSSg\nEEIIcVSHInH2RWJ9dLfeuY/T6eS5555D0zQikQgHDx7kvffe46677mLatGnccsstSV8rEolwxRVX\n8OKLLzJ06NBeKe/RrFy5koqKCubMmQNAXV0dd955Jzk5OcydO5esrCxef/115s6dy2OPPdZi8bPe\nJAGFEEKIAc9oNOJ0Opv+HjJkCOPGjePMM89k/vz5jBkzhilTpiR1rW3bthGL9VWA1VZJSQl5eXlN\nf69duxav18uTTz7JkCFDALj99tv5+OOPWbVqlQQUQgghRG+75JJLeOutt3jllVeYMmUKgUCAFStW\n8OGHH+L3+3G5XJxzzjncfPPNOBwOVq9ezaOPPorBYODaa69l3LhxLFq0qMPzbrrpJrKysgA9k7Bk\nyRI2bdpEdXU1LpeL888/n5tuuqlpaXKfz8fSpUvZsmULlZWVFBYWoigKl19+OQDXXnst5eXlgL6M\n+aJFi7jiiiuYOHEiubm5TY/LYDCQk5NDIBDos7qUgEIIIcSgNnHiRJ5++mm8Xi8rV65k/fr13Hff\nfYwcOZKysjIefvhhFi5cyNy5c7nkkkvw+/2sWLGCpUuXUlhYCMBTTz3V7nmLFi1i7ty5ADz55JPs\n3LmTefPmkZeXx549e1i4cCGxWIyZM2cSjUaZNWsWkUiEWbNmUVBQwLp161iwYAFms5lJkyaxdOlS\nbrjhBoqLi7n99ttxOByYTKYWwQTofSp27tzJj370oz6rRwkohBBCDGqNzQSVlZXMmDGD6667jmHD\nhgGQn5/PRRddxBtvvAGA1WolIyMDgOzsbOx2O8AxzwP46quvGDduHGPHjm065vHHHyce1zu8rlu3\njv3797No0SLOOOMMQM9IlJSU8NJLLzFp0iSys7MxGAxYrdYWTTjNhcNh5s+fT3Z2NlOnTu3Rujoa\nCSiEEEIMatFoFACTyYTBYOC1115j06ZN+Hw+YrEY0WiUWCxGJBLBYrG0e41kzps4cSKvvvoq4XCY\nCRMmUFxcTEFBQdM1tm3bhtls5vTTT29x7eLiYtavX08wGCQtLe2oj6WhoYFf/OIX7N27l4ULF+Jw\nOLpZO8mTgEIIIcSgVlpaCuiZitmzZ+P1ern11ls5+eSTsVqtvPbaay0yDe25++67j3nejTfeyOjR\no3n77beZN28eAOeddx533HEHubm5NDQ0NI0gaa6xA2hlZSXDhw/vsAw+n4977rmHmpoannjiCcaM\nGdOl+ugqCSiEEEIMauvWrcPtduPz+di1axezZ89m8uTJTfvD4fBRz9+9e3fS502aNIlJkyYRDAbZ\nuHEjS5YsYf78+Tz++OPY7XZsNhvPPvssWuuJOOCoQ1Rra2uZNWsWBoOBxYsXk5+fn8xD71EyU6YQ\nQohB6/XXX2fHjh1Mnz6dSCQC6H0jGtXX1/PRRx8BtPmQb/w7mfNCoRBr166lrq4OgLS0NC688EKm\nTZvG9u3bASgqKiIcDhMIBCgsLGz6sdls2O12TCZTh4/jN7/5DfF4nN/97nf9EkyABBRCCCEGgXg8\njs/nw+fzUVlZSUlJCQsWLGDx4sVMnz6diRMnMmrUKBwOB6tWraK0tJSSkhLmzJnD+eefD8Dnn39O\nKBTC4XCgaRqffPIJu3fvPuZ5X3zxBdFolOXLl/PQQw+xbds2vF4vW7Zs4d1332XcuHEATJgwgdGj\nR/PAAw/w6aefUl5ezsaNG7nzzjtZuHBh02NxOBxs376dHTt2UFVVxdq1a/niiy+4+eabicViTY+z\n8aevGNpLqwwwZwKfer3epigy1VgsFvLz80nlMqY6qcPukfrrvoFch9/bVdmHM2W2b5TFxBsn5h77\nwHY88sgjrFmzpsW2rKwsxo4dy9SpU1tM/LR582YWL15MeXk5hYWFzJgxg6KiImbOnElZWRnz58+n\nqKiIe++9l61bt3LiiSeydOlSNm3axDPPPHPU84YOHcqyZcvYsmULDQ0N5OTkMH78eH7yk580dZ6s\nrq5m2bJlbNiwgdraWnJycrj00ku5/vrrm+aqWLNmDYsXLyYSiTBnzhw2bNjQ5vGBnhkxGAz8/e9/\n71K9wZHXNXAW8NnRjpWAIgUM5DeiviJ12D1Sf11XGo5RG49jNptxuVxUVVURjUZxGI2MsHacoj6e\ndLSWRyQW51BtmAKHFYup+wlvg0EfadHTa3mIrutMQCGdMoUQoouqonG+t7uSIx+13qZ/mYA1J+Xh\nMh//LcsdfZB7Khq44e1SFv3QjXtIRrfvI4Ht8U0CCiGE6CKX2cgbY3KpjcfZF9P4eamfh0Y4GWUy\n4DAaB0QwIUSyJKAQQohu0Js1TGTG4OSMNMamWRk1MFo6hOgUCZ+FEKIHnJRmYd34Uzgprf2ZFIUY\n6CRDIYQQoktG56Tx0vRTGJ5t6++iiBSQMgGFoiijgN8B3wLCwDvAnYAL2A0EE4caAA34laqqi/qh\nqEIIIQCb2ciJuen9XQyRIlImoADeBDYDI9GDiDeABcADgKaqave7EAshhBCiV6REHwpFUbLRg4mf\nq6oaUFX1IPACerZCCCGEECkuJTIUqqpWAzNabR4FHEj826AoygvAJPTh3c8Cv1ZVtX+nbhNCCCEE\nkCIBRWuKopwN3AZcCYSAj4HXgB8DxcDr6P0s5iZ7TbM5JR8qcKRsqVzGVCd12D1Sf90nddh9Uoep\npzPPRco9a4qiTAT+Atyjqur7ic0XNDvkn4qiPAj8nE4EFC5X6k/ZejyUMdVJHXaP1F/3DcQ6rHr3\nXmJ15b1+nxhwOPFvQ6t9JvtQXJMe7vUyiK5LqYBCUZQrgZeAW1VVffkoh+4BhnXm2o3z66ei1msA\niM6TOuweqb/u2xONM3tfFQtHuRg9wGbIjFcfhIayfi1DLBbD6/Ue+8B2PPjgg7z99tsYDAY0TcNi\nseByuTjttNO4+uqrOfPMM3u4tH1v165dPPfcc/zrX/+irq6OESNGMHXqVK6++upuXbfxvSGpY7t1\npx6kKMoE9I6YU1VV/Xuz7ZcA56qq+mCzw4vQg4qkRaPRlJ8b/ngoY6qTOuweqb+ua4hqfNUQpCES\nJaK1/n59fDPSNmPQ1zTo8mszHo/jdDp57rnn0DSNSCTCwYMHee+995g5cybTpk3jlltuSfp6kUiE\nK664ghdffJGhQ4d2qUzdsXLlSioqKpgzZw4AZWVl3HbbbZxxxhk8+uijpKWl8eabb7JgwQIAvvOd\n7/RJuVIioFAUxQSsAOY0DyYSqoD7FEXZA6jAOGA28GifFlIIIcRxy2g04nQ6m/4eMmQI48aN48wz\nz2T+/PmMGTOGKVOmJHWtbdu2EYv135iAkpIS8vLymv7+5JNPMJlM/PrXv8Zi0Wdqvfnmm/noo4/4\n+OOPB1dAAZwHnAI8qSjKU+jBaOMEVm7gB+j9JVagBxhPqKr6RP8UVQghxEBxySWX8NZbb/HKK68w\nZcoUAoEAK1as4MMPP8Tv9+NyuTjnnHO4+eabcTgcrF69mkcffRSDwcC1117LuHHjWLRoUYfn3XTT\nTWRlZQFQV1fHkiVL2LRpE9XV1bhcLs4//3xuuukmrFYrAD6fj6VLl7JlyxYqKyspLCxEURQuv/xy\nAK699lrKy/X+LGvWrGHRokVcc801XHPNNW0em9FoxGTqu4VlUiKgUFX1I/ThoB3ZD6zqo+IIIYQY\nRCZOnMjTTz+N1+tl5cqVrF+/nvvuu4+RI0dSVlbGww8/zMKFC5k7dy6XXHIJfr+fFStWsHTpUgoL\nCwF46qmn2j1v0aJFzJ07F4Ann3ySnTt3Mm/ePPLy8tizZw8LFy4kFosxc+ZMotEos2bNIhKJMGvW\nLAoKCli3bh0LFizAbDYzadIkli5dyg033EBxcTG33347DoejzeMJBAL86U9/4vDhw9xzzz19Vo8p\nEVAIIYQQ/WXIkCEAVFZWMmPGDK677jqGDdP7/efn53PRRRfxxhtvAGC1WsnI0Cduzs7Oxm63Axzz\nPICvvvqKcePGMXbs2KZjHn/8ceLxOADr1q1j//79LFq0iDPOOAPQMxIlJSW89NJLTJo0iezsbAwG\nA1artUUTDkB9fT3f//73CQaDjBgxggULFlBUVNQrddYeCSiEEEIMao0jm0wmEwaDgddee41Nmzbh\n8/mIxWJEo1FisRiRSKSpj0JryZw3ceJEXn31VcLhMBMmTKC4uJiCgoKma2zbtg2z2czpp5/e4trF\nxcWsX7+eYDBIWlpah48jIyOD3//+9/j9ft5++21mz57Nb3/7W84+++weqKVjk4BCCCE66VAkhj8W\nb7FtX0wDYFcoQjTackyE02SkwNJ3bdmic0pLSwE9UzF79my8Xi+33norJ598Mlarlddee61FpqE9\nd9999zHPu/HGGxk9ejRvv/028+bNA+C8887jjjvuIDc3l4aGhqYRJM01dgCtrKxk+PDhHZbBYDBQ\nWFhIYWEhRUVFVFZWsnjxYlauXNmleuksCSiEEKITDkViXLO7krDW/v6fl/rbbLMa4M9jciWoSFHr\n1q3D7Xbj8/nYtWsXs2fPZvLkyU37w+HwUc/fvXt30udNmjSJSZMmEQwG2bhxI0uWLGH+/Pk8/vjj\n2O12bDYbzz77LJrW9gXW0RDVL7/8klAo1CYTMWbMGDZu3HjUsvekgTX7ihBC9DJ/LN5hMNGRsEab\njIZIDa+//jo7duxg+vTpTfNcZGdnN+2vr6/no48+AmjzId/4dzLnhUIh1q5dS11dHQBpaWlceOGF\nTJs2je3btwNQVFREOBwmEAg0ZRoKCwux2WzY7fYOR2ysXr2aBx54oE0As2PHDnJzc7tWMV0gAYUQ\nQogBLx6P4/P58Pl8VFZWUlJSwoIFC1i8eDHTp09n4sSJjBo1CofDwapVqygtLaWkpIQ5c+Zw/vnn\nA/D5558TCoVwOBxomsYnn3zC7t27j3neF198QTQaZfny5Tz00ENs27YNr9fLli1bePfddxk3bhwA\nEyZMYPTo0TzwwAN8+umnlJeXs3HjRu68804WLlzY9FgcDgfbt29nx44dVFVV8YMf/IBgMMj999+P\nx+OhtLSUF154gX/+85/8x3/8R5/VsaG9tMoAcybwqdfrTdkZAC0WC/n5+aRyGVOd1GH3SP0lb2sw\nwvS9VZ0+7+UTXIxNa79DX6ozfPYYhmBl798HvWNkLBaj9SeTlpaLdubdyV2ooQKiDU1/PvLEs6z5\n4ONmd4GsrCzGjh3L1KlTOeuss5qO3bx5M4sXL6a8vJzCwkJmzJhBUVERM2fOpKysjPnz51NUVMS9\n997L1q1bOfHEE1m6dCmbNm3imWeeOep5Q4cOZdmyZWzZsoWGhgZycnIYP348P/nJT5qGf1ZXV7Ns\n2TI2bNhAbW0tOTk5XHrppVx//fVNc1WsWbOGxYsXE4lEmDNnDhdeeCElJSU8//zzlJSUADB8+HCu\nueaapCfr6kjjewNwFvDZ0Y6VgCIFyJt590kddo/UX/IGY0DRV3rkdRiuxbjuDgxtQhLQDEbiFzwB\n1rZzN4j2dSagkE6ZQgghBg6rg/iER/QMRTwCoSqwucBoAXOGBBO9SAIKIYQQA0vGkP4uwaAknTKF\nEEII0W0SUAghhBCi2ySgEEIIIUS3SUAhhBA9ZMCPmWtld2WA6S9tZXdloL+LIlKAdMoUQohuitks\n1LhHEnZmklF6GPueMgzxgR9ehGMau31BwrGB/1jFsUlAIYQQnTTqYCnZdbVoQCg/m4YR+Wh7d8Be\nfb/JHCJzTzmWOv2be7XdASe4+q/AQvQBCSiEEKITLD4fn1x3NaZ48mtzRI0m1m/cDIXtL+4keknI\nj6H0fbQRF4PN2d+lGfAkoBBCiE4I5eRQ/M4/sGTZ0Mz6Yk2mhiD2PWWY6kOE87KoH5mPZtbfXo3B\nCFFfAwtzcvqz2INTyI9x9ypi+cUSUPQBCSiEECJJ+00m5hS4+PLEE/QNcY3MfeVk1pRjGH5i03Gx\niJnaMSMI5R/5EHusto5FdbU4B/5yB51WGo5RG49jjmq4ahuoCoSJRqM4jEZGWGXJ9+OFBBRCCHEM\nceDFjAzmZ2XRYNQHx5nrAmRt24eltu0IB1M4SvaWPYTys6k9eQRxq4V3HHYuzkjnwepqLg8G+/gR\npK6qaJzv7a7kSAOSt+lfJmDNSXm4zDIg8XggAYUQQhzFPpOJu5xOPrbZADBqGul7y8ncU47hKNkG\nA5DmrcZaVUft14cTHJZDhcnEjJwcrgwEmF9dTX4n+mH0t7LaMNWBaItte3zBFr+by043M8xhPeZ1\nXWYjb4wkGipCAAAgAElEQVTJ1TMUZjMul4uqqqqmDIUEE8cPCSiEEKId7WUlTolEuL38MPN2lyd9\nHWM0RvbWffxai/D0kDwOmUz8NT2dj2w27q+uZmogkFhQO3WV1Yb54R9KOhweev+avW22WU0G/nhd\nUVJBhd6sYdJXtnRk4A3WE4mkeq2I1iT0E2IQKw3H2BqMUBII86/aBkoCYbYGI5SGY/1dtH61z2Ti\nB7m5/MLppMFoxKRp3Flby1teL+5wuEvXHB8I8n5FBdfV1wPgNxq50+XiupwcDphSu59AdSDa6bkm\nwjGtTUZDDGySoRBikBqsbdcHqkPUhdoGTHabiYJsG3/IyOCBVlmJx/1+To9Eun1vh6bxUHU1VwcC\nzHY62WM2szYtjYutVn5ZU8N/NTTItzxx3JKAQohBajC2XfsDUX7whxLam8TSaIAhd3+LjU4HACZN\n47a6Ou6srcXWw+U4NxzmvYoKFmZlsSwzk3qjkV84nfwlPZ3H/H5OjA3uDFGPMVrQMgvBaOnvkgwK\nElAIMYgNtrZrZ7qZV68roi4UY48vyP1r9nLf5BPYUuhkRZ6T3Tl2AMYmshLf6IGsREfSgV/V1HBl\nIMBdTidbLRY22GxMGjKEeYe288Oaw7RpCDFnQMaQXivTgGMfTvy8B/u7FIOGBBRCiEFleHbLfMMz\nY4by6Zh84EhWYmZtLR11JXSajFgNEO5ElwKrQT+vPeMiEd7yellst/OEw0FGqIbpf78Dk9Z2BIhm\nMBK/4AmwOpK/uRB9RAIKIcSgEwfeTEsD4EurHjokm5UosJj485hc/LGWH/j7Yho/L/Xz0Agno0wt\nszxOk5ECS8cdL63Az+rq+E4wyGynk/O+t5LscB1u/z6e/vgRgqf+FEtmoZ6hGMDBhDcS4/ndh7jC\nZkDmtTz+SEAhhBhU9ppMzHY6+VQLUoA+r8TM2lruPEpWorUCi6lNgGCO6imLE20Wvm7uWrOROxpl\n1eHD/D4zk0ddR9b9uG3EqdxkzOPMXmyCSQXeaIyFe7x886R8nF2sQ9F/Bl6vKyGEaEcceD4jg2/n\n5/OJ7Uizx5N+P3d3IpjobSbgpvp63vN6OT0xRHWv2cx38/KYm5VFwCAftCI1SUAhhBjw9ppMKLm5\n/DIxr4RZ0/hRYj6Ir0VTc66EMbEYD1dXA5ARj6MZDKyw27k0P5+PrakS/ghxhAQUQgi8kRiP7T6E\nNzKwhivGgZUZGVzaLCsxNhLhb14vM0IBxuSkYTWl7jf+xjfoZVVVfDux/sdesxklL497srOpkWyF\nSCESUAghEm3X5XijAyeg2JPISvzK6SSQyEr8LDHb5WnRKGNy03n5P8cyJje9v4vascxCYuc+QH76\nMJ73+Xi6qgpXYo6KlzMzuXjIEN619fQsGUJ0jQQUQogBJQ48l5nZoq9EY1birhTqK5EUkxXsw8Fk\nxQBcEwjwgdfL1Q0NAJSZTFyfm8utTieVRnk7b6PuAMZPfgF1B/q7JIOCvAKFEANGY1bi19nZ7WYl\nepPNYODkjDRsvdwMkReP84zfz8rKSoYlshVvZGRwUX4+q9LS6NyKG6lhb12Qp7cd4tb1XwFwoD7U\nMxeORzDUH4T4wB4dkypk2KgQ4rgXB57PzORBh4NA4pt6USTC41VVvR5INDopzcK68afg9XqJ9MHw\nzsmhEOMrKpiflcUrmZn4TCb+JyeHNwIBHqyupiDFl0avCUdZc8jPm/t9fOnXMy5YzRCKUBdJzY6y\n4ugkoBBCHNf2JOaV2JBo3jBrGnfU1XH78da80QXZmsZj1dV8NxDgHqeTfWYza9LT+cRm49c1Nfyo\noSGllkaPxjU2eGt5s9THP8qrCTdbVMUITHRlcuPYURRnGPskKBM9SwIKIcRxKQ6szMzkoX7MSqSK\nC8Jh/u718ojDwbOZmdQajdzjdLIqsdjYCT2w2NhIfxlZwbqkj69JswNuALbXBPhrqY+3D1RxONTy\nufmaI42rRuRw+XAXBY4M8vPz8Xq97VxRpLouBxSKohQAxUABoKqqWqsoSpqqqsEeK50QQrRjdyIr\nsbFZVuLO2lpuq6sb8FmJjmRoGvfX1HBVYrGx7RYLH9tsXJqfz5zaWn5cX992sbEkWap8fLD8p+2u\nL9KRqMHI/ItXc/82M9tqAi32Oa0mLi90ceXIHE7JSscgw18HhE4HFIqiWIFngOvRs1Qa8L6iKLnA\nPxRFuUBV1X09WkohRK/qqw6F3dWYlXjQ4SA4yLMSHTk7EuEdr5cnHA4W2+0EjEbmZmfzl/R0Fvr9\nnNyFeoq4crjop8uTylDE0yCeCVUuO/uqI4DedGE2GLhgaBZXjchh4hAHFhmVMuB0JUNxP/A9YDaw\nFvgksb0CKAEeBP6zR0onhOgTfd2hsCt6OiuxuzLAr97ew/zLR6fuXBQhP4bS99FGXAy25JfLsgH3\n1NZyRSDAbKeTL61WPrNauSw/nzsSdWbpZFH2O4e1u10DNCvEHBCzA60mChubnc5VI3K4bLgLl7UX\nW9mDlRCubbmt/mDL381ZHZCW23vlGYS68uxOB25WVfVPAIqiaACqqjYoijIX+FvPFU8IMdg1zivx\nUA9nJcIxjd2+IOFYCg+0DPkx7l5FLL+4UwFFo1OjUf56+DDL7HYWOhyEDAYWZGXxt/R0Fvn9nN6N\n4FEz6QFEzAGatVVmK6phqoOHzh/Nt0e7unyPpAUrMa6/F0MHw0NN/17eZptmtBCf8LAEFT2oKzmn\nPODTDvZVAAN3bV0hRJ/abTIxLTeX32RnE0zMKzG7pqZP5pUYKMzArXV1vFtRwTdD+vwOWy0WrszL\n04fZduJamgFimRAeBqFREM01HAkm4hrGOg3LIQ3bPrD4YGRGH83iGa7tMJjoiCEeaZvREN3SlYBi\nF3BxB/smAvu7XhwhhNCzEr9PzHbZ2MRxaiTCW14vs7qQru8LO4MRvrVxGzuDqdlkdFIsxmuVlTzg\n95MZjxMzGFjscDB5yBA2HmWxMU3T+Ko2QCRPDyIiQw3EMwyQ6G9jCGqYvXoQYa0AU4CUGqoq+k5X\nmjxeB55SFGUE8C76a+c0RVGuAOYCv+u54gkhBpvdJhOznE42NesrMbOL7f59KaRpfNUQJKQ5SNWP\nVCNwfUMD3w6FmJOdzQdpaewym/mPvDz+u76eX9TUYNf0JqCDDWHeOuDjr6VV7KsPQVazxxTRmzRM\ntWDswUTRzmCEqRu38WhhFqO6OiRF9JuuBBS/BQqB+4DfoP/PeQOIAc8CD/RY6YQQg0YceDYzk4eb\n9ZU4NdFX4lRp3uhRI2IxXvL5+L/0dO7PzsZvNPJCZibv2mxM3VHKrn/v45+VrUZ0xDWM9YkgItg7\nIdPxEJSJjnU6oFBVNQLMUBTl18A56H0mqoB/qqpa0cPlE0IMArsSIzgasxKWZiM4UjkrcTwzAEog\nwLeCQW5Lt/OJ08FBs5mnThlNmtGM45OtGEMRzs61c3ZWJivXlmFI4f6rov91eQyPqqqHgL/0YFmE\nEINMDH0ER/OsxGnhMI/7/RT1YFairDZMdaDl9fb4gi1+N5edbmaYY2BPkbWvPsRfS338rdTHoUCE\n7DHDqJ14KvEMG8GTR2AaPZRfV1bxX1oUT0UDz2tl/V1kkeK6MrHV+mMdo6rqhK4VRwjRH/qj7XpX\noq/E5l7OSpTVhvnhH0o6HB56/5q9bbZZTQb+eF1R/wcVRgtaZiEYe6ZGaiMx3j3o56+lPr6oqm+x\nL29/Bdd8Eqf0zJN5z5VFvdXCvQVDWBcI8GNfD63+KQa0rmQowtBmhVw7cApQBmzubqGEEH2rL9uu\nY+h9JR7JyiKYGCnQG1mJRtWBaKfnmgjHNKoD0f4PKOzDiZ/3YLcuEdP0Bbn+Wurjg7JqQq0W5Do3\n38FVI3K4cFg2aSYjBOr4IB7hnuxsDpjNvJWezkdjR5F+dg22f5Ym/eqwmgxkp8tyUYNJV/pQXNTe\ndkVRnMCLwJvdLJMQYoDamegr0dtZCQG7aoO8WerjrdIqvKGWQ1lPcqRx5YgcvjPcRX5a25q/KBRi\nrdfLQ1lZPJ+ZSY3JRM20Mzhn8tf42X4vwxLLi+/xBbl/zV5+M/kERuektbjGYGg2Ei31WPioqqo/\n0VHzVeB/O3u+oiij0Iecfgs9C/IOcKeqqjWKooxL7BsHlAPLVFVd1FNlF0L0rr7OSvSHfLOJ2aOH\nkm82oI9Z6Xv+cJTVB6r4a6mPkupWC3JZTFw23MVVI3IYm33sBbnsmsYDiaXRZzud7Dab2ZyVyYyx\n6fyipob/bmhoOnZ0ThruIRmdKuuhSAx/rGU97UtkknaFIkSjLcvnNBkpsMhY0lTW0/moCDCyi+e+\nid5cMhJwoQ9FXaAoyh2JfcuAy9GbVtYoirJLVdU3ul9kIURv2mkyMcvl4p+JyZMsiXklbh1gWYl8\ni4m7C/MT66H0XUARiWt8XFHDm6U+PiyvIaodadIwG+D8IVlcOSKHC4ZmdWlBrvHhMO9WVPC4w8FS\nu50Go5FfOZ38JT2dWw93bXHpQ5EY1+yuJNxBS9TPS/1ttlkN8OcxuRJUpLCudMqc3M5mA5AP3Ars\n7sI1s9GDiZ+rqhoAAoqivADcDlwBWIAHVFXVgM8VRfk98FP0oEMIkYJi6LNdPtosK/GNcJhFAygr\n0V80TcNTE+DNUh+rD/ipCresz1MSC3JNKXThsnX/e2M68IvaWq4MBpntdFJisbDJZuPfOTnkAZ19\nNv2xeLvBRGG8EgtR9hqHttkX1vTzOgooTBUNGBqSL4mWYSaW9NEiGV15pa1G75TZXr6sCn3xsE5R\nVbUamNFq80jgAHAW8K9EMNHos3aOF0KkiPayEj+rreV/BlhWoq8dDkZ4+0AVb5b62FHbMjuQazPz\nneEurhyRw9ezemf11NMT058vttt5wuEgkggUZzqd/M4c7/QEZAYtzmnxvXw79jmTYp9TpO0jjoFp\ntl/ymenrSV/HWOUn956POzVPhmY0UD7RTzyrU0UWR9GVgKK9dTw0wA/sUFW1oZ39naIoytnAbcB3\ngR+gByrN+YCczlzTbE7d3saNZUvlMqY6qcPkHQpHqWrVdr03kaLXf7dNi7tMRgqSWHo6BixPT+eh\nzMymrMTpkQhP1tZSFIuBpe/Dia6+JsxmM5ZOlLe3XoOhWJwPyvz8Ze9hPq6opvmAFavRwMUFTr47\nKo8JQ7IxG3t/dkkLcHcoxHejUW6Lavobv8XCd/KzuL2hgVkNDRx1SbB4mNyGz3kg/AGXxj5nmNay\neSOEhaCh/c6cHT0nWn4elY9O7HSGwpifh6kfXpPHk868nrsyyuMfnT2nMxRFmYg+YdYcVVXXKory\ng3YOM9B26OpRuVx9sIRuNx0PZUx1A70O91U2UNPO4lNZaRZG5R67U1xpMMxVG7e2GDrY3D37Ktvd\nbjMa+Hj8WEakddxr3wP8GGicqMaCPjf/PRYLlpxOxf89qjzctZEGLpeL/PzsYx63NxCiOqonz/fX\nNoDZBmYb2WYTJ6R3bbVNTdP4rMLPq18dYNXOg1S3atI4a4gT5eThXH1SIU5b/3wg5gMvBKq5GkgD\nagwGHs/MZHVmJs8C5zU7Vgv5iFV8SLz8A+KH13NKLMgpzfZHMLHBeArvmYpZYzqTQ8b2lxR3uVzk\nO9q+ziNGP75OdgoFyHG5sOTmd/o80b6kAgpFUTozEFpTVfWXXSmMoihXAi8Bt6qq+nJisxf4WqtD\nc4D23/k6UFVVRTRF223NZjMulyuly5jqBkMd+hsiXLb0c9qLBUwGWH1zMc6Mo3+47AyEOwwmjiYU\n19hZ4cWW3vbDOQYsS0/n4Q6yEm271/WtqlYTOCV/XhVea/jox0RjXLStrN0xHSbg/VOG4TIn34mw\nLBDmr/sOs2pfJXvqgpxoqGSl5W/cZbiChrQCrhqZy1Wjchnj0Js0IjV+vJ14TD0tWBtgTG46i+pq\n+F04nQ+tVrYCE+Nxflm5jTsOrSW9aj2muhIMrb4DVpPB+6YzeM9UzD9M36DWcOyAoKqqCm+w7fOp\nVbdOYienqqoKQ7w/azD1Nb63JnVskte8txP314BOBxSKokwAXgCmqqr692a7/gncrCiKUVXVxv+3\n5wAbO3P9aDRKJJKaywo3Oh7KmOoGch1mWuDV64qoC7XtSma3mci0cMzH3p1gS6/blin1HYm+Ep92\n0FciFZ6Jrj7mZF5LduCNMbnUxuNtglqH0Yhdix9zxEcgGmNtWTV/LfWx6XBdi49dhzHOSUYfc79R\nQNHIsRgTAVuqvMZHZpl5ebqea/hfbzn/iO6kvG4z3/au4+v1+9ocH7MOI5J9LjvSz+Z73gKihs4l\nydt7DSZ20JWxH9FoFFKkLgeCpJ5NVVU7P9aoExRFMQEr0Js5/t5q91tADfArRVEeA04HfgL8qDfL\nJEQqGp7dtRR6T4sBKzIzeazZCI7TE/NKnDJAM0QdGWE1ASYsFgv5jgy8wfr2P/SaiWsan/vqebPU\nx3sH/TS06tNyVk4mV47MYbI9Cz6F05wZcIx5I/pFrB5LzWdYqzdgqdnEf8RarlAax8Bm5zfY75rA\nuWnjyLCNAoOB6mCE6OGuZRVE6urR3kOKogwFXlZV9dudPPU89PklnlQU5SmOjCLRADdwJfo8FD9H\nn977XlVVV/dYwYU4zhyuj/DGl4f53jfyyMvs2zb0HWYzP3M6+axZVmJWbS23pPAIjpH+MrKCdcc+\nMKEmzY7+1tOzShsX5DpQxYGGls0pIzKsXDkihytGuBiekQgca2p7vAzdZQxXYKneiLV6A+a6f2HQ\nWgaQmsFGJKuYTbnnc9sJk/Fk6ENAh8ViPOT3Mzkk64IMVF0KKBRFOQP4NtC854wBOBM4t7PXU1X1\nIzhmxuqCzl5XiIGqsj7Cc5vKuODE7D4LKGLA0sS8EqHjKCthqfLxwfKfYtKSn2wqajCy4QefQBc6\n+rVWF4nx3iE/b5b6+NzXsv3fbjYyqcDJlSNzGOfKPObslf1C0zAFduhZiOoNmAO72hwSN7sIZ48n\nkj2eiGMcGNMoAv5Uo3EfDfw5I4Myk4kbcnP5biDAf5Uf7vvHIXpdVya2ugZQ0QOA1vNR7AN+3TNF\nE0KkimiGjZmFw9iadmQNjlmJvhKpPlA34srhop8u73SGYp6r6yNTYprG5sN1vFnq4/1DfoLNOsIa\ngPGJBbkuGpZNuqlXW5S7Jh7GUvv/sNRswFq9EWOkbR/4aNpoItnnEs4+l1jG18HQ9nHkxOM87fdz\ndSDAvU4nZSYTf0lP54ORhdAQJ628qpeXohN9qSvvBb8CHgV+iz4C4wygAbgBvbPk8h4rnRCiX2lA\nw8h86sYUUJn44DsjMdtlKmclWtvvHNYn99nur+P5f+/nzX2VVLQa3jvGbuOqxIJcQ9oZLdPfDNFq\nLNWbsFZvxFL7KYZ4y4mzNExE7d8gnH0ukezxxG3J1+mkUIjxFRXMz8ri5cRiYxSdQHCIk6yvSjGF\npGPkQNCVgOJkYJqqqkFFUTTAoKpqGfCQoiizgKfRgwshxHEsmmGj5pRRRLIzAT0rMTvRVyLVsxJ9\nqSIQZs0hP+8c9PNvf8t5/bIsJi4rdPHdkTkUHW1BrmAlhNvpL1F/sOXv5qwOSGt/voZkGYOlR5oy\n6rdiaDUANm7KJJJ1TqIp42w0s73L98rSNB6trubqQICZ2dkctFgI52VT6bRj33GQ9EOVkq04znXl\nfcHAkWaOWmAosDPx9+vAL3qgXEKIftI8K0EiK2GuaeCZaj/fkXWZAKgKR/n7IT/vHKjiM199i6Ge\nJgNMHJLFVSNyuGBIFtZjNWkEKzGuvxdDvONv6aZ/t038akYL8QkPdy6o0GKY67diqd6AtXoDptCB\nNofErEObmjKi9tOgk0M7j2ViOMzy0kPcc6AMW7qpafSK2VCPfW85pnYmbgOotjvghIE9cd3xriuv\nlM+BOYqi/AzYir6AV+PkeN+k/TU+hBDHgdZZCeJx7LvLyNhfwZhRLjCl6jiO3lcfjfFBWTXvHPSz\nwVtDtNX8YKe7Mvn+KaO4wGUjqzPdIsK1Rw0mOmKIR/SsxrECilgDltrE0M7qzRhjNW0OiWa4E00Z\n5xJLOyHpIaq7KwP86u09zL98NGNyk18/xFFZyb8uPA9TvBMdZY0m1m/cDIVtFw4TqaErAcUDwCpg\nIbAE+L/EdNk+4FTg5aOcK4ToZ6MOlpJd1zK9rgHBYS4a7Hmw0wOAuS6AfXcZpmBY/3Y4avB9OwzF\n4nxUUcOag37WlVe3mWX0a440Lit0cVmhkzFOO/n5jcuX92+fAEPYq/eFqN6Ipe6LdoZ2Wok4ihOZ\niG+iWbrWATUc09jtCxKOdW721UhODuf9YRXZdbVoBgOBghwCBbmQWIvE4dmPtaZl81G13cFD/TiF\nuzi2rqzlsVpRlLHAAVVVv1IUZSr6CqNW4FXgiR4uoxCiFavJwJicNKymziUELT4fn1x3dae+GcLg\n+naoAfF0WLKrjM8+q6Mu2rKuRmRYmVzoYkqhk6/10qqenaZpmAI7j8wPEdjR5pC42ZkY2nlu09DO\n/rSvcESLvyPxNGq/NoJYmhVXOA1z4OjTnovU05Vho2eoqvr/Gv9WVfXPwJ97tFRCiKMak5vOy/85\nttPnNX4zzKqrJeKy0zA8l1j6kQ+W5lmJ5gb6t0MN0GwQs+s/mAysO3ykaSDPZmZSoZMphS5Oc2ak\nyHwRGpaGEizVf8NSvQFTpO3cDtG0ExJZiPHEMtztDu1MFZb6IDmftw2ExPGjS30oFEXZAvwBeEVV\n1Xa6HwshUlEc+OqMU6kfPYyo/ci3a1MgROaectJqfBhGndR/BexDGqBZE0FEJmBpGSRkmoxMLnRy\n2XAXZ+XaMaVAEGEwxDFbQlisIczWMIZDT7XYr2FMDO0cnxjaWdhPJRWDUVcCih8BCjAPfajoB+jB\nxeuqqnZtWT8hRK/SgHfS0ngwN49q25E5EIyBEPY95aSV+zB0fhHS41LcAvFMPZDQrK2ChLiGsQFM\ndbD0ypM4dVhm/xSyGaMxitmqBxEmc6RNf8m4MYNI1tl6U0bW2WhmR/8UVAx6XelD8Ufgj4qiOIDv\noQcXvweWKIryBvCiqqrv9GwxhRBdoQHv2mwsdDjYYm0WSATDZO4pI72sCoM28CMJzXSkOUOztfpE\n1o4EEcYGmgIrs7G/MhIaJnNEz0JYQpjMbVeXjceMRMI2QifeSHTIt8E4eEffAGB1oBktnRotoxkt\n+lweosd0eYCxqqq1wIvAi4qiOIGrgTvQVweV0epC9CMN+LvNxiKHg//XLJDIj0YJ7jxE+iHfgA8k\nqkJR3jvk5897Kwmd0E4QEQBjPZjqwdC5Pqq9II7ZGsZiCWG2hjAa2z430aiZaNhGJGwjHjMDBmIZ\nYyWYAEjL1efkaD05WP1BTP9eTuzUn0Jmq+afHpgYTLTU7RlLFEX5JvB99GzFScBn3b2mEKJrNOCD\nREbi82aBxLBYjNtrayn2VXPDwV5eNrqhAqINbbebMyBjSK/eui4S44PyalYfqGLj4Vpaj2Y0BDVM\ndYkgou0X/z5lCB/GWrMRS+X7WHK8bZoyNA2iESuRsI1o2Iampd73tNxMCz/+5jBy+3jF23al5XYc\nIGQWQtboPi3OYNTV1UbPRQ8ipgKjgF3AK+hLl3t6rnhCiGRowIc2GwscDj5tFkgMicW4va6OH9XX\nk4Y+E12vCtdiXD8HA22/YWsGI/ELnujxNHMwFufjihpWH6jio4qaNnNFjEq3crA0hLEejP25/Iim\nYQrsSsxSuRFzYPuRfYlgIh43NGUhohEbqT5PYF6mhRnnFvR3MUSK6Mqw0f1AIfrCYCp6ELGxpwsm\nhDg2DfjYamWBw8Fmm61pe34sxq11dfxnfT19OlOC1UF8wiMdZyh6KJiIxDU2Ha7lnYNVvF9WTX07\nc0VMGe5iSqGLSCDODf/up+858Qjmun8lJpnagCnibXNI1FJAtKaaaNhGLGoh1YMIITrSlQzF++iz\nYb6nqmo/Jw2FGJx2VwaYtXovsR+ewad5R9K8ubEY/1NXx383NJDeX30keqlZI65pfOGr552DVbx7\nyI8/3PLtJ99mYXKhkynDXS0W4vIE2gluelO0BqvvEz0TUfNPDPFAi9360M7TjqzaGQpj2jS3b8so\nRC/oyiiP63qjIEKI5Gy0WnnQbsFbGeCQUf8vnBOLcUt9PdfX15MxgDpbaprGtpoAqw9U8e5BP2Wt\nFo7Ktpi4tMDJlEInxf04V4QxeADL4c2EdnyK3fdZm1U7NWM64cZVO7POaTm0M7SnbwsrRC+RVYiF\nOE5stlhYmJXFhzYb1lA1BYA9HuemmhpuqK8ncwAFEnvqgqw+UMU7B/3srQ+12JduMnLRsGymFDo5\nN9+BxXj02R+z081YTYZOrTdhNRnITj/K26MWw1xfkpjqeiOmUKm+mSMNFjHLECLZ4xOrdn5jQI3G\nOFAdoi7UNkFtt5kYnm1r5wwxGEhAIUSK+8xiYaHDwQdpR6bItifW4njB5+NMU4qsJ9FNhwJh1hzU\nlwTfVtOymcBiNHB+fhaXDXdywdBs0o+1JHgzwxxW/nhdEdWBlj0y9/iC3L9mL7+ZfAKjc1qua5Gd\nbmaYw9pimyFWj6XmU33BrZrNGGOthigChuxTCdrPIWg/h1j6mKRX7TRVNGBo6FyPUS3DTH+0OfsD\nUX7whxLi7cRnJgO8OeMbOI8WjPUlowUts3BABXOpLEWedSFEa18kAom1zQKJrHicn9bV8S2fj9vh\nuM9KVIWivHvIz+qDVXzhaznRrhH4Zp6Dy4Y7uWRYNg5L19+uhjmsbQKERqNz0nAPyWh3nzFUpmch\najZirvuyg1U7xxHJHo+WO5G84WOp8XqJdWK1UWOVn9x7Pu70TKWa0UD5RD/xrM6d113OdDOvXlfU\nYYYiZYIJAPtw4uc92N+lGDRS6JkXQgD8KxFIvNcskHDE49xYX8+MujqyNQ1PFwMJp8mI1QDhTp5u\nNT3Sc9wAACAASURBVOjn9oTaSIwPyqpZfbCKTe3MFXGGK5PLCp1MKnSSa+vjb5ZaDHPDV3oWonoD\n5uDeNofEza6mtTKar9ppsXStrHGXk8pHJ3YpQxF3Obt0z+6SZg3Rnq7OQ5ED/BQ4EygAfgiUAZer\nqvrXniueEIPHFrOZRQ4H76QfacKwx+P8pL6eG+vqcPVANqLAYuLPY3Lxx1p2Gvz/7d15fFx1ufjx\nzzmzZZmZZJKuSfeyCRSBAoWCqIi0oFdA8Ysi4HrRK+6IAnJV0J8LXvXqdbsoXuACwter4IKFomxC\nWYSyLwJdKE1amiaTZJJMZjvn98eZNJPJJJ01M0mf9+vVV5pzzpz5zkky55nv8jzbUjaXbu/l2wua\nWZSjJHqzy2S+p8DESgMdmM/8FGvFhQzXz+eB1/u5o9PJFRHP6i8/IFjH2rYQp7SFaGvI3ZNQMVYM\nT+8TztLO/kcxk33jDknWL3PmQwRXkWrYv+xVO1MT9JAIMZ0Uk4fiIOA+IAg8BxwG+ID9gFuVUipd\n0lwIkYfn04HEuoxAosGy+MjgIB8fGCDeH2NXNMmujMds7Rke8zVTrvH/TPM9rnHBgTvp3OCX+Tzs\n7y7PSolkKo5vsJOfv7CNm7q7x+WKWJjOFbGmLcSyQN0EZ6kMM96FJ7wRmEPg5UsI+LeN2W8bbhJ+\nZygj0XQMlreyGT6FmAmK6aH4PvAMcI7WepdSKgKgtf6nUupy4BJAAgoh9uLFdCBxe0YgUW9ZfHhw\nkE8MDtJqWeyMxHnf9c9PuELhivXju+S9LoObzz940qCiUizb5omeQe7oCLNtxyZ+5YK/74owaDuv\ncU5dOldEW4g3ZOSKqDjbwhV9BW/fw3j6HsEd3Uz9QBvwKQycoQbL3UQieIwznBE4AlzSayBEIYoJ\nKE4A3qq13pVj3++Ar5XWJCHytz2eImJZuJM2ocgQ4WicZDJJwDRZ4K292gcAL6cDiT/V1WGnb6h1\nlsWHhob4t4EBZlmjn+T7osmCljsCxFM2fdHklAUUtm3zQl+UOzrDrO/sZVc6V8RBhgUuCLhN3jO/\nlbXtIY5oacScqiDCGsYTeTK9tPNRzGRPzsNiLW+nf8kbSTYcAEZt/s4IMR0UE1AkgYlSz9UDVa/b\nJ/YN4aTFGVu6M37hRtMau4D1y2cRcpd3rLsUr7hc/GcgwG319aOBhG1z3uAgnxwYYI41vf50NkeG\nubPTyRWxLStXRIPL5IRZAeiDnx67HHfTwilpk5HoSae5fgRP5EkMe2y7bFwk/SuINx1L3DqSpa8O\nYM87nWTjzFh6K0Q1FRNQPANcDpybsW3kI9QnkGqjYoqE3Ca3LW11eijcbkKhEOFweE8PRa0EE5vT\ngcSt9fVY6UDCZ9ucOzjIhQMDzJ1GgUTnUJz1nWHu6AzzUv/Y+Rte0+D4OUHWtoU4YW6Q+sFt8Ci4\nK9kjMVJwq/8RvH0P4x56edwhlstPInhMOkvlSmxXIwCLgRvPHXe4mALFrDYq50ojURnFBBTfAf6s\nlFoF3A14gCuUUgfjTNBcW8b2CTEpZ1jDhcfjYXagga7hQRKJ2iiutDUdSPy+vp5U+qbqtW3OGRri\nU5EI86dJINEdS3BXZy93dvbyVHhsrgiXAUfPCrC2rZm3zmsmUOhKkGJYcTwDT6eXdj6Ss+BWyrdg\nz9LOZOPBMpRRY4pZbVTqSiP87aU2W+xFMbU87lBKnQRcCpyF07u8FngY+KzW+sHyNlGI6WWby8WP\n/H5+29CwJ5Dw2DbvGxri05EI7TUYSPgMgwMa6vCl2xtJJLl7Zx93dvTy6O7IuHHMw0ONrGlv5u3z\nm2nxeWC4G6KvQWaCy8HOsV8zeQNQ1zp++wSMRC+e/n84kyojGzGssb0jTsGtQ0gEVxFvWoVVtyDv\nc4vqmJLVRlYCY7ATrPwTjYniFZWHQmt9P3B/mdsixLS23eXix34/tzQ0kEzfmN22zdlDQ3xmYIAF\nqdotzru8zsMdK/fnd8+8wo+27eaBrn4SWbkiDgzWs6atmTXtIebXZ0z4HO7G3HAJxgRv2q7nrh63\nzTY9WKu/M3FQYdu4hrfh6XsYT/8juAdfxGBseyxXI4nAShJNx5IIHjW24JYQYsoVk4diJ3Ck1jrH\nxw4hqmPTcIL3PPIiV7UFWTTFvdsdpsl/BQLc3NBAIh1IuGwblQ4kFtVwIJGwbB7dHeHOHX3cs3Mj\ng4mxbV3c6NsTRCz1T5ArIh6ZMJiYiGElIB4ZG1BYCdwDz+LtTw9lxHeOe1zKO49E07HEm1aR9B8K\nxgxI9usNYJuegq+hbXqcnh4hakQxf427cDJkSkAhakbMtnlpaJiYHWC03mNl7TBNfhIIcFNDA/GM\nQOI90SifjURYUqOBhG3bPB0eYl1HmLt29BKOj035PHckV0R7iIOClc0VYST78fQ/li649RimNXYB\nmY1BsvENe7JUWnWL8i64NW3UtTq9NfHxxcYY7MT13NWkDrkAGtvG7itw2EiISismoLgSuFIp9Sbg\ncaA3+wCt9fpSGyZErdppmvzU7+fGxkZi6Zubadu8Oxrlc5EIS2s0kNgUibKuo5c7OsJ0RuNj9jV5\nXJy+XxtvndXIiqCvorkiTDOJ2xvD3fED3MObMLJmaNhmPYngkcSDq0gEj8b2VKdexZSqa508OGhs\ng+CSKWuOEMUoJqDQ6a+HZ223cT4a2jgTNYWYUXalA4kbGhsZTt9wDdvmzHSPxH41GEjsiMa5syPM\nuo4wL0fGTmSsc5m8dW4Ta9ubeVNbC21z59LV1UWigEqZ+bFxuRN4vDHc3hguV/o6DY8u8Ux55qTT\nXK8i4V8B5tRn+RRClKaYgOKtZW+FEDVst2nyM7+f6xoaGDaddfCGbfOuaJTPDwywf7KwKpGFWti7\nk+DwQN7H99X7uWvXXK56pYMnskqCuw04dnaQU9tDvHlukAa3E/t7zDKv7zcsPJ640xPhiWGa4xMO\nJH1LiLecSKLpWFJ1S6Z8KGNLd5TL123lm6cuYWmrJLaqBdmrjcT0Usyy0fsq0RAhak23afJzv59r\nGxqIZtxw3xmN8oVIhAMrHEgAeMI93Hv1Bbjs/JeaJk2Tw/a/gR5/055th7c0cmpbiJPbmgl5KzOR\ncWQow+ON43LHx8UHtg3JhI9E3Ecy7iV59Jer2o0fT9ls6RkuOLW5qJzldR7uX3VQ+XrKfM1YS08H\n3z4wbFYDilnl8a29HGJrrb9SZHuEqLoew+C//X5+3djIUEYgcVo0yucjEQ6egkBiRCLUwlsuuHrC\nHgrLB1YDWPWA6dzB++ob6fE3sV+gjlPbQ6xpa6atwVf+xtkp3IMv4ulej7dpNy73+CEfyzLTAYSP\nZMLLVE2YFQIAXzP28jOr3Yp9RjEfVS6ZZF8fTmobCSjEOB19MQZi4286fp+L9qb8b3g7EqmcGfYA\nNscSJJPFZdgLGwZX+/1c09jIYEYgsTYdSBw6hYFEptea5+35vw3YPkj5nX9kZRMkYeMagO8eupi3\nLwnl/Rx5L7tNDeGJbHTqZfQ/ipnsd7ZnvJOkkm4Scacnwkq5kSBCiH1DMUMe4wZblVKNwHE4lUY/\nU4Z2iRmmN5rk7Oufx8rRu+wy4E8fW0Fz/d5/HXckUpy5pXvCGgCXbh+36AivAbcubZ0wqOgzDH7p\n9/OrxkYiGYHE24eHuSgSYUXZJykWzvJkBBGerBt0ygkiXANgxJzb96ICeyQmW3ZrxnftSXPtGXgK\nwx4bWNmGm2TMJBn3kUj4sC2Zk102pge7sQ1MT7VbIsRelWUwVWs9CPxVKWUDPwGOL8d5xczRXO/m\nlvMPnrCHIp9gAqA3ZRVUUAicAkS9KWtcQNFvGPyqsZFf+v30ZwQSJ6UDicOrHEh0DsX5Y2cPsXaw\nfVlBhGVjDjpBhBktcx+AbeEaejldtfNh3MNbxh1iuZtJBI9x6mXQivcv/44xlMRFfr04doOb2lsT\nU4P87VjH7W2UWYjaUO7ZWVsYv5xUCICChjUqKWIYXNPYyNV+P30ZgcRb0oHEkVUMJMLxJH/t7GVd\nZ5gnR1ZojAQTto05lA4ihsAo51zCdJZGb8cNNPevw0yGxx2SrFuyJ0tlquEAMJxrZ776JK1ferCg\n9timwevH92IFy9J6IUQNKGZSZq4F4gYwG7gY6Cm1UUJUwoBh8D+NjfzC76c3I5A4MR1IHFWlQCKa\nTHHv6/2s6wjzcFc/yawbsxFND2kMglHmumKu6BZ83XfSEH4JvJfgCT+IaTvBhG24SfoPc3ohgquw\nfHNznsMKNdN91fEYQ/nPMbEb3FghmXkvKmRoFySHxm93N0DDnKlvzz6imB6KYWCizyIGcFnxzRGi\n/GzT5JamIP/XHCTsGh32OD4W44uRCMfE45M8ujISls3DXf3c0dHLPa/3MZw1yXT/9AqN/X11XHzr\n5rI+t5EaxBu+F1/3nbiHnORShrEYAMvdSCzwtnSWyiPB1ZjXOVNzGsraxqnQ2ujhI8fMo7VR5ifM\nKPEI5oYvjysmB2AbJtabfiQ1UCqk2NTb2T8pGycF96Na64dLbpUQZWCbBkPtsxhcNIdfekdvGsfF\nYlwUiXDcFAcSlm3zVHiQOzrC3NXZS29WIa759R7Wtoc4tS3EfkEn0dLOSByvyygoV4LXZdCUPSfF\ntnEPPIOvez3e3gcw7NjoLsNDsukoiEP0wKsYzGNFzEwwq9HDx46dX+1miAxlKfLnDWCt/u7EPRQS\nTFRMMQHFr4HtWutxna9KqSal1Cqt9SOlN03MVJXOUGibBkNtsxhaNAfLNxpIHJPukTh+igOJl/uj\n3NER5o7OMDuiY4dVmr0uTpkf4tT2EIeFGsYV4poX8HLz+QfTFx07nLC1Z5gr1r/K105ZzJKWsVVA\nm+rdzAs4I5NGogdf91/x9azHFesYc1yyfhmx1lOIh04ihh82de2ZFyFENZStyJ8Ma1RFMQHFFmAe\n0JVj3yLgDiD/BfBin1OpDIW2aRCd38rg4rljAglP3wDfHBrkA3ZqyjIidA7FuKPTKcT1SlYNjXqX\nyVvnNXFqe4hjZgXwmJO3al7AuydAyLakpY4Ds4cb7CSe3ofwdd+Jp/8fY4pvWa5G4qG3EGtdQ6p+\nvz3prmdjctGSucx2G0CZJ2oIIfYJeQcUSqmvpv9rAF9USg1mHWLg5KKQjziiohZ1bqdpYLTUs20Y\nxGY3EZ3fgjWwC57bBYA7EqV+x26iKRcrj1uBUVfZsfJwPMld6SDiyfD4Ghqr5wQ5tS3EiXOD1LvL\nP6xgDm/H130nvp6/jVulkfCvINa6lnjzajDrxj12tsfFxW2z0ymPJaAQQhSukB6K5TgBg42zmiOX\nKPCNUhslxEQ8PT08dP7puKxCalu42PDIP6At9yqFUgwlU9y7s487OntzrtA4sqWRte0hTp7fTHMl\namhYMbzdDzq9EYPPjd3lbiHW+nZiradg+drK/9yi8gY6MJ/5KdaKC8HfXu3WCDGpvN/htNYfBFBK\nWcBiYFeOY2LZ24Qop0RLC8dd/wcCw0MMLZhNbPZoASz3YJT6zm48vYNjhjb6/AG+3dJSvjZYFg91\nRbijI8y9r/ePW6FxQLCOU9tbWNPWzLz6CpThtm3M6KsABF75Cv7G0cRTNiaJplXEWteQCB4FxuQ9\nIdvjKSKWhTtpE4oMEY7GSSaTBEyTBd59Y3JmTbMSGIOde/KECFHLypJ6e4RSqgX4ntb6oyW1SogJ\nWMCLK9/IwPL52B7n19dIJAm80kldXw/GnEaowHwsy7Z5smeQdR1h/rqjl76sFRrtDV7WtoVY297M\n8kBlSmEbyT68PXfj616Pf3cc+NSe1RopXzux1jXEWt6G7ckveAonLc7Y0p0xY2J0WpQLWL98FiG3\njGAKIfJTVB+sUmoeTnrt1ozNBrAKeB8gAYUou+fcbj7X1kKkbnQOQF1nN4HNnZiJ8idytm2blyPD\nrOsIc2dHmJ3DYz8lhrxuTmlrZm17iMOax6/QKE8jLNyRJ5zlnn0bMupoOEMYieAq+pevJtl4COPq\nhe9FyG1y29JWp4fC7SYUChEOh/f0UMz0YCKWtOjoi9He5MM3w1+rEFOhmEyZJwJ/Bvw48ylG3sVs\nIIVTy6NgSqk1wHXA3VrrczK2fxBnqerIcIqRfq4TtdaPFfNcYursjMRzLnnM/Jopc8njiAHD4PuB\nANc0NpJK3zRdA1GCL23H25c9N7h0nUMx1nX0ckdnmE1ZKzQa0is01ua5QqNYZvx1fN134e2+C1di\n7OhisuFArOBpLN3qJbXgPJL+4ntEnGENFx6Ph9mBBrqGB0kkZnZ10JGqt9lLbwuteivKb7bbJauN\nprFieij+H/Bb4HvAY8BpQAL4UHr/RBM2J6SUuhj4CPDSBIfcp7U+qeCWiqraGYnzvuufn3B56BXr\nXx23zesyuPn8g5kX8GID6+rq+GpTEzvSGS7rLAv3lp00vLarrLUswrEk63eEefy1LWzvdyqWeoCD\nDGeFxgGtrRy9cBknzm2i3lWhT7NWHG+fs9zTHXlyTKY/yxUk3nJSernnEtqBG5dXphkF8wawTQ9G\nAeP8tumpSoKhXFVvR34PC6l6KypDVhtNb8X85awAPqK1fjldXXS71noz8JBS6jvAdyg8qIgCxwA/\nBuQjwgzRF00WnGsinrKdxzXXc3lTE3/LGN44JRrlg13dfGHb7rK0b3BkhUZHmId3RwjYUe72XY3L\nlyNl74CJNetHUIFgYqSehrfnHsxU/+hzYpAMHEGsdQ3xpmPBrMAEz3Koa8Va/R2IR8ZuH+zE9dzV\npA65ABqzVpl4A1DXylQrV9VbIcR4xfz1+BgdfhgEWoCRYgO/Ah6gwIBCa/0TAKXURIcsVEqtB47C\nKT72Na31jYU1u/bIDPvxbJfBjXNC3Dg7xHC6gFd7Msk3+vtZMzzMDtvGa1BQCXOvAc3pQCBhWWxI\nr9C4b2cfwxkfVXup57Pej3PKbC/HB4ZpefX3WPsrqJ9T/pS9qUF84fvS9TTGdsylvHOItZxCvPVk\nLG/5l7pWRF3rxAFCYxsEl0xpcyYjwxpCVEYxAcVzwEeUUt8AXgHOxxn6AGc56fisOaXpwhkKuRR4\nAXg38L9KqQ6t9b35nsTtrq1PHuFkijO27Jpwhv09B80jVIHkR1Op0Gs+vKyFntMP5Zq5zo3bbdt8\nIhrlosFBGgE8HhZ5PPx5/7mEs5Zqvpqw+NK2bq5a1Mpiz9hehCbTYEdkmP95bSfrO3roz5rAuaDR\nx2kLWjhtQSvL0zU07L4t2P/cjTvQhtG0tLAXPhHbxjXwLJ6udbh77sOwsupphI4nMftUUsEjwDBx\nZjdMjZGfVTn/Tmy3Gzt9TsMz8wtwVeQaNs7C3u/duBtnzehr+Fo8SSRl4bJT7IgM0R9PkUrZBFwm\nCyuRv0XkrZDf52J+Uj8ErgduAa4FrlZKrcS5I54E3F3EOSektf4L8JeMTbcopc4EPgzcm+95QqHa\nygY+G3ioOURfcnzXa5PbxeL66f8p6vV4fl30qUYv4dPewODKBXu2nQD83DA4tKEBGsamlp6d4xxP\nR4ZgWzeHz2nlsEADtm3zfE+E373cyW2bOukcHDu5cla9l9OXzefd+7Vx5JzmcSs0EmYvPTi/N57W\nXM+YP3t4N6mOP5J67TbswbHzRozA/rgWnomr/R0Y3uqX8y7n30k5r+F0Ut73mtmw8IAynq/2dMeT\nvPPBZzM+XO3c8z+XAU+vPpRWCSqmhWLyUNyolNoGbNVav6CUCgAfAJYBvwEuL3Mbc9kKrCzkASPL\n4WpJQ/pf9pI9gK6BqjatLMLhyVdg2AYMHL2Q3rUHYTU4wYc5GOficD+f99mY5C4Yk0t/3AnMXnl9\nN396KsxfXusev0LDbXLy/BCnLWxl1ewgbtMAkuzePX5Oht0XTr+GMIaVbysyT5DC1fuI0xvR+8iY\nehq2q4FEy0kkZq/FajzQWe7Zlyjg1ZZfrt/BUpV8DaeZSlzDfcWfD5jr9FC4XASDQfr7+0mlUgRc\nJlZfuIp/GWLk9zqvY4t5Aq313zP+/0OcXouKUEp9HOjRWv82Y/MbGJ23kZdkMkkiUZvZ5roSKX7V\nu4N3+AyaZ9BSqcneVOPzA3SfsYL44tFf1MZ/vEZo3QusedcyUnMayDezRCSR5PaOPgA++eA/ITb6\nc3YbBifMCbC2PcSbMlZo2Kkkk6auSCZxjbyGAn5vzOGOdD2Nv46vp9F4qDPBMnTCaD2NGrvxlPXv\npMhrON3V8ntNrZpnwDy3gcfjyli+bAG2XMtppNjEVm7gLOBIYD5wkdZ6l1LqCK31E+VsIM4k0B8r\npTYDTwHvBU7FWRUyI3QlU3x/axfHLJ9Ns7uEHABDuyA5NH67u6FmyvlaXhe9bz+AyOole1ZMeHZG\naLn1GepeDU/+4CydQzFu2rKb27Z1M+R2wSKnW90AVrb6ObU9xNvmNRGsdHepNYw3/AC+njvxDDw7\ndpc7RKzlZKeeRt2CCU4ghBDTXzGJrdqAu3B6CXqAZuBrSqlZOEtHT9Fa31/gOaM4yao86e/PBGyt\ndYPW+sdKKT9O7ot5OOXTT9daP1lo22e0eARzw5fH5C4YYRsm1pt+VJV1/3vaAAwdOo/wvxxMqsmZ\n+GjEkzT99WWCD2zBsPJftvFMeJAbNnfxtx29o/056Qms5y2fyznzg8ytRA2NTLaNa+glJ4Nl+F5M\nazSQc+ppHJNRT6P8Ac2W7iiXr9vKN09dwtLWyqT6LpnpwW5sA3PmTiasmGnw4UCIbMW8030fiANH\naK2fUkpFALTWzyulfgJ8HWdyZt601pO+I2qtvwV8q4i27ju8AazV33XehLLX/5d7yWMBFvbupMGb\nIPLm/RhearBw0wsA1G3aTfC+zbgGxtaT66/zAweOO0/Ktrl3Zx83bO7iqYy5GS4D3ja/mTcvnsNX\ndg/wzkWtzC20l2e4O2cOhTFf04zUAN7oU/j6HsA9vHVsG33txFpOIdZ6ct71NIoVT9ls6RkuOM/H\nlPK3Yx0nf7YFq/EPB0JMpJiA4hTgXVrrp3Lsuwb4eGlNEkXL/uRS7fX/vT3c+8sLnFLj/5XfQ5KG\nycNnPwRznJUdQ8kUf3yth5u2dLF9KL7nuEa3yRkLW3n/0tm0NXjZloIDhpL4Cq2nMdyNueGSCbM8\nup67GrBxe+J4fFE83tiYkhm24SMeOoFY6xqSjYcWXE9DiHEyPxxkq+KHAyH2ppiAwk3mup6x7CLP\nKWaYDV4vXzz2cMyXX6a5txdSFv4nOvA/+hpGjqWyI/rr/FwZaqFrOMHNW7r43bbuMXkj5tV5eP/S\n2ZyxqJWAZzRLw/I6D/evOiidsreASVzxyITBhGGm8PqieH1RTNfYybJJ32Jic95FPPRmbFdj/s9X\nosw6FDBaD0XqUMwwMqwhpqFibv4vAJ8ELsqx72ycxFczzsgbeS7yZj5qt2lyZTDI70ZyRyxbhm9L\nDy23PYP3dTfMmjxJlOWFn23awUM9AyTt0S7fg5vqOW/ZHE6a31yxglwOG7c3htcXxe2Jj+lwsCyD\nRKyOeKyexMpLprz3R+pQCCFqWTHvPj8BrldKHYEzOdMNfEwpdSBwOvD+MravJuR6I88kb+ZOXcAb\nGxr4djBIXzpldjCZwnPrszRu3D5pIS8bsOoh1QRWg8Hfu535DAZw4twg5y6bw5EtjZUpD55mupJ4\nfVE8viimOdpY24ZkwksiVk8i7mO0uO7UkzoUQohaVkxiqxvSNTcuw6k8CnAJ8Cxwbla+iBkh+408\nV9njUt7MfYbBAQ11hY//14hn3W4ubW5mo3d0ZcX7BwdRm1/nc49vn/BxtgEpvxNI2N7R1+41DU5f\n2MI5S2ez2F/uTO4ZUoP4+v+OL9iN2zM2H4SVMonH6onH6rGt2kmBLj1hQohaVWxiqxuAG9JZMoNA\nr9Z68rSI01yuN/IlLXUcOKchx9GFKXr8v8oGDIP/CAS4prERKx0MHZRI8J2+Po6Ox/lnKneSLtuE\nVBCSTTjdOyOSNu5++MnblnNUe4Umntk27sHnnOWevfc79TQ8e3aRiPtIxOpJJrxUszdCCCGmm7wC\nCqXUZcAPtdbRrF3HAfdrrYdzPExUS4XX/9vAX+rq+GpTEztdzqf3esvii5EIHx0cHLk/01Tvxusy\n9ixttDxOb0SqDtwD6RMBRtzG1QuuAfC5DBYEy/8p3Ej04Ov5G77u9bhiY3tNUkk38VgdiVg9tl3+\n8uRCCLEvyLeH4hs4pcmzA4r/Aw6nwDTYosIquP7/VZeLy5uauLtudChibTTKlf39tKfGju3PC3j5\nzXlv4KFdEW7fEeaJPqcTy4jZuHth2Zw63rN8FiuCDXvmRzTVu5kXyD8p1aQl4D3g6X8MX/edePqy\n6mmY9cRCbyZWdxg8fS3SG1EBAx2Yz/wUa8WF4G+vdmuEEBWWb0Ax0butvAvvI2LAL/x+fhwIMJy+\n+S9IJvlGXx+nxGLjjk9YNus7w9y4uYsX+0fjUI9pcPzcIA939HHFkYtLGjIKJy3O2NI9QQl4i0fi\nXyWQfG1suxoPceppNL8JXHXQvxWX/BpXhpXAGOyECZblCiFmFpkWLvZqg9fLpU1NvOJxBjPcts3H\nBwb43MAADfbY5Rv98SS/39bNzVt3s2t49EbS5HFx1uJZnL1kFt39CR7e2Fdyu0Juk9uWtjo9FIZF\nU+qfDHfchXvwZYIMMct2AgzL3Zyup7FG6mkIIUSFSEBRBK/LYGlLHV7XzP5kOy6nBLAqFuPbfX0c\nmFUls2Moxk2bu7jttR6iGZMxFzX6+MDS2bxzYcueSp/d/eX5xGrEu1gW2Yin/3G8kccxUmPracSD\n6XoaTcdUpJ6GmMBIHYrs9OVSh0KIGU3eZYuwtLWeG899Q7WbUTG5ckqEUin+vb8fFY2OGSB4KjzI\nDZt2cc/OvjGF149saeS8ZXN409wgZrmWw1px3APP4ok8jqf/cdzDr44/xNfG8J56Gq3leV6R3ZAK\n7QAAGwFJREFUvxx1KJz05VKHQoiZLt+AwoYclWpG94kZ4lm3m0uam3kiI6fEOYODXNrfT0t6eCNl\n29yzo48btuzi6fBor4DLgJPnN3Pesjkc3Fz6clpsGzPWgaf/cTyRx/BEnsGwx8/XSHnnkWo+msZl\np9OTWkRiktTeosKkDoUQ+6xCJmU+o5TKTizQADyslMp8B7e11jKluwCbhhO855EXuaotyKIq5VAa\nMAy+Fwjw64ycEm9IJPh2OqcEwGBGoa6OjEJdfrfJmYtaed/S2cwvtWx4ahBP5Ek8/RvxRB7DFd81\n7hDb9JHwv5FEcCWJwEosXxser5dg62zo6gIkoKgqGdYQYp+Ub0BxXUVbsY+L2TYvDQ0TswNM9cIZ\nG7i9ro6vZeSUaLAsLsrIKfF6NM7NW3fzu1d3M5AcjSnn1Xs4Z+lszljYit+TfyQ0Zg6KbeGKbnJ6\nIfofxz34AkaOgCBZt9QJIIIrSTYeDGaJgUsG164hjKHk3g9MsxvcErIIIUSWvAIKrfWHK90QUUZ5\nrv/fW06JF/uGuGFzF+s7wyQzBrYOaWrg3OWzedu8ZtxFFOpaFhzmt6d14un/E57OjZjJ8Ss+LFeA\nROAIEsGjSASPrNh8CDPcS+uXHpy01kg22zR4/fherGBFmiSEENOSTMqcifay/n+ynBInDw/z4K5+\nrtjcxWPdA3seYwBvmdvEuctnc3iowEJddhL3wPN4Ihvx9D+GO7pp/CGYJBsPIhE4kkTwKFIN+4FR\n+fEfK9RM91XHF9xDYYWaK9gqIYSYfiSg2Mc8mM4psSkjp8QnBgb4eF8/97zWzVlbutg6MDrxsc5l\n8q4FTqGuRf78U2KbsZ17VmN4Ik9iWNlJViHlmbVnHkQycDi2uzoT9lJlqMcihBD7Ogko9hFd6ZwS\nv8/IKXFsLMaXu7rZ+PIO3vtqN+H46Kf0WT43Zy+ZzVmLW2ny5vFrkhrGM/D0niDCFesYd4hteEj6\nV5AIriQeWIlVtwimaYVVIYQQY0lAUYQt3VEuX7eVb566hKWt9dVuzqRy5ZRoSaX4xK5uup/czGc6\nwsSt0QkE+wXqOG/ZHNa0NeN1TVIoy7ZxDW/NmEz5LIY9ftgg5VtIIugMYyT8h4JZwXLkQgghqkYC\niiLEUzZbeob3VNEsxI5Eit6sst7b0ufZHEuQTI79xN7sMplfwAqKTJtcLj47a9aYnBJv7+7F+/AL\nXNMRHnPs6tkBzls2h2Nm+SecH2EkI3giT+DpfwxPZCNmonvcMZbZQDJwRDqIWInlnVtU24UQQkwv\nElBMoR2JFGdu6SY+QRxy6fbecdu8Bty6tLWgoGLIMAgAnw6FeCodTLQPDTPrwWd5estoXgePaXBa\ne4hzl81meSBHT4udwj30UroX4jFcQy+Pqdg5ItmwP4nAyJLOgyZMc93RF2MgNn7Bpd/nor2p/CXL\n8+INYJsejAIKWNmmRxI0CSFEFgkoplBvypowmJhI3HYeN2FAMdwN8QiQzikRaOU3zUFuBizDwJtM\n0vLEyySe3MrOdKbLZo+L9y6ZhVoyi1afZ8zpjPjujMmUT2CmBshmuUPp1RgrSQSOwPbsfcVDbzTJ\n2dc/j5Xj9bsM+NPHVtBcX4Vfx7pWrNXf2XMN9xjsxPXc1aQOuQAa28bu8wagTtJ6CyFEJgkoprPh\nbswNl2BYCbb653Ppqk9xT/uBzBnq5j8OO5e+3QmCd9yPPTiMASw2ejjP/TSnnvAB6htnO+fIoz6G\njYuk/5A9vRCp+qVgTDK/Iofmeje3nH/whD0UVQkmRtS1ThwgNLZBcMmUNkcIIaYjCSims3iEOPCz\nFefwoxXvZ9jtDBvsTtXzq50H49u2FRdwlLGd89yPc6K5BdOwsaPH4hncsNf6GE5myqNI+A8DV+lL\nK6s2rCGEEKLiJKCYxh5sCHLpv/yCTU0LnQ2WRcNTm/FvfAV3Kska8yXOc2/kYNdO3O44bm8cjyeG\n+doV486Vqz6GLOkUQgiRLwkoJrEzEqcvOn4p5Nae4TFfMzXVu5kXKF+diVy6TJOvBgL8MWNs39PZ\nTfCB52ju7ea9rqf5YMOjzPP14vbEcLkTOWODStbHEEIIsW+RgGICOyNx3nf985MuDb1i/fj5Bl6X\nwc3nH1yRoMICfubx8YPmJmIe50dnRGMEHn6RlZse4/MN93J86yvUeaOY5vh2W5ZBMuElvuBsEnNP\nqVh9DCGEEPseCSgm0BdNFpVnIp6y6Ysmyx5Q/HE4yb+HQuwOOcsV3VaCE5+8i/M23cYaz7OE5g+O\ne4xtQyrpIZnwkkz4SCXdgEEqeBxIMCGEEKKMJKCoZbaNNZzgQ0mT7fstYPFwB2e8uo41HfdxUvgx\nGhiGrLmSVsokmfCSSPhIJrxgF7YaQ6SZHuzGNjA9ez9WCCGEBBQ1ybKw+6O4gm6OPHSAkyN3c/L9\nGzhgMMeSTpt0D4TTC2GlXDi1QUVJ/O1Yx32r2q0QQohpQwKKWpJIcmDkFU4wnueE1tdYPfQMvmfG\nZ3DcUx/DvRDrWY0EEEIIIapNAooacNLwY6yNPcqJ5gvMq+t3NmYkqIyZDRi56mP0b8UlwYQQQoga\nIAFFla0Zeoj/5heQMVRvYfBE0xu41/NGNgwu46LFKzmovrarmgohhNi3SUBRZTsIMWj4iHj9/HXW\nav46ezX3eVaQ2DKAeygGBtgTFNsSFTC0C5JD47e7G6BhztS3Rwghpgm5U1XZI8tWM+ugswEwEkkC\nr3RS93p33j8Y164hjKHxybcmYje4GV9NQwAQj2Bu+DIG45cL24aJ9aYfSZVRIYSYgAQUVebpHcDb\nO4BrcBj/lp2Yyfxv92a4l9YvPYhRQLoM2zR4/fherGARjZ3pvAGs1d+duIdCggkhhJiQBBSTWNi7\nk+Dw+PLdk+mv8wMHTrh/Ued2mgaySmU//dSEx/f5A7A4lHOfFWqm+6rjC+6hsEJ7Lze+z5JhDSGE\nKIoEFBPwhHu49+oLcNlWQY9LGiYPn/0QzBlfndPT08ND55+Oy8r/nEnTxYZH/gFtc3PuT+V4HiGE\nEGKqSUAxgUSohbdccHVRPRRXhlpyn7OlheOu/8P4HopJ9PkDfLsl9/mEEEKIWiEBxSRea55X9nNu\na1tQ9nMKIYQQ1SaFHoQQQghRMgkohBBCCFEyCSiEEEIIUTIJKIQQQghRMgkohBBCCFEyCSgm0FTv\nxusqvJKn12XQVJ978Uyzy8Rb4Cm9hvO43DsD2KYn974J2KZHMj4KIYQoO1k2OoF5AS83n38wfdHx\nWSi39gxzxfpX+dopi1nSUjdmX1O9m3kBb85zzve4uHVpK72psYmttqVsLt3ey7cXNLMoK4hpdpnM\n97hyN7KuFWv1dyCelddisBPXc1eTOuQCaGwbu88bgLrW3OcTQgghiiQBxSTmBbwTBgcAS1rqOLDA\nTJXzPa5xAYI76RTjWObzsL+7wC6MutaJA4TGNgguKex8QgghRBFkyEMIIYQQJZOAQgghhBAlq5kh\nD6XUGuA64G6t9TlZ+84GLgOWAv8ELtNa3zX1rRRCCCFELjXRQ6GUuhj4T+ClHPsOB64FvgTMAn4I\n3KqUass+VgghhBDVURMBBRAFjgE25dj3UeB2rfWdWuu41vom4Bng3KlsoBBCCCEmVhMBhdb6J1rr\niWp6rwQ2Zm3bCBxd2VZNzOsyWNpSV1Seilx8hsEBDXX4jPKcD9OD3dgGBeaoEEIIIYpVM3MoJtEK\nhLO29QAHF3ISt7t8L/WAeR70hw8r2/kOrHdz/6o2wuEwyeT4vBcFCy2BE7/HBNkrZqSRn285f877\nErl+pZNrWDq5hrWnkJ/FdP2pGYBdyANCoVCFmlI+06GNtU6uYWnk+pVOrmHp5BpOT9MhoOjCmYyZ\nqSW9PW9l+/RfAW63m1AoVNNtrHVyDUsj1690cg1LJ9ew9oz8TPI6tsJtKYfHcOZRZDoa+E0hJ0km\nkyQSibI1qhKmQxtrnVzD0sj1K51cw9LJNZyepkNA8UvgUaXUqcDdwAeA/YEbqtoqIYQQQuxREwGF\nUiqKMyfCk/7+TMDWWjdorZ9TSn0AJ0/FIuB54B1a611Va7AQQgghxqiJgEJrXb+X/bcBt01Rc6av\noV2QHBq/3d0ADXOmvj1CCCH2GTURUIgyiEcwN3wZI8fiF9swsd70I6d0uRBCCFEBElAUYUt3lMvX\nbeWbpy5haeuknSt52TSc4D2PvMhVbUEWFZs8whvAWv3diXsoJJgQQghRQRJQFCGestnSM0w8VVAq\njAnFbJuXhoaJ2QGcFBtFkmENIYQQVVITqbeFEEIIMb1JQCGEEEKIkklAIYQQQoiSSUAhhBBCiJLJ\npMw8dfTFGIilANjaMzzmq9/nor3JV7W2CSGEENUmAUUeeqNJzr7+eaysRR1XrH8VAJcBf/rYCprr\nC7uc2+MpIpbFtvRqkc2xBMmkQcA0WeDdl4qPCyGEmO4koMhDc72bW84/eE8PRTa/z1VwMBFOWpyx\npRsrY9ul23sBcAHrl88i5JYRKSGEENODBBR5KveQRshtctvSViKWNa5kb8A0JZgQQggxrUhAUUXO\nsIYLj8fD7EADXcODJBIlJLYSQgghqkQ+BgshhBCiZBJQCCGEEKJkElAIIYQQomQSUAghhBCiZBJQ\nCCGEEKJkElAIIYQQomQSUAghhBCiZBJQCCGEEKJkElAIIYQQomQSUAghhBCiZBJQCCGEEKJkElAI\nIYQQomQSUAghhBCiZBJQCCGEEKJkElAIIYQQomQSUAghhBCiZBJQCCGEEKJkElAIIYQQomQSUAgh\nhBCiZBJQCCGEEKJkElAIIYQQomQSUAghhBCiZBJQCCGEEKJkElAIIYQQomQSUAghhBCiZBJQCCGE\nEKJkElAIIYQQomQSUAghhBCiZBJQCCGEEKJkElAIIYQQomQSUAghhBCiZBJQCCGEEKJkElAIIYQQ\nomQSUAghhBCiZBJQCCGEEKJkElAIIYQQomQSUAghhBCiZBJQCCGEEKJkElAIIYQQomTuajcgH0op\nC4gBNmCkv/5Sa/3ZqjZMCCGEEMA0CShwAogDtNavVbshQgghhBhvugx5GOl/QgghhKhB06WHAuC7\nSqnVQAD4LfAFrfVgldskhBBCCKZPQPEQsB44H1gGaOCnwIfyeGwdgM/nw+2uzZfrcrmA2m5jrZNr\nWBq5fqWTa1g6uYa1Z+RnQvpeOhnDtu3KtqYClFJrgT8CjVrrxF4OPwe4sfKtEkIIIWasDwA3TXbA\ndA0BtwIuYA7QsZdj78S5EFuB4Yq2SgghhJhZ6oAlOPfSSdV8QKGUOhw4V2v9xYzNB+MsI+3M4xTd\n7CWqEkIIIcSENuRzUM0HFMAu4AKl1C7gP3EipSuB/9ZaT7/xGiGEEGIGqvllo1rrTuA04AxgN/AA\n8BfgS9VslxBCCCFGTctJmUIIIYSoLTXfQyGEEEKI2icBhRBCCCFKJgGFEEIIIUomAYUQQgghSiYB\nhRBCCCFKJgGFEEIIIUo2HRJbzWhKqUU4CbtOBOI46U0/q7Xur2rDpiGl1A9xrp0EygVSSn0FuBCn\nmu9DwL9qrV+tbqumD6XUG4EfAEcCUeBvwOe11rur2rAap5RaA1wH3K21Pidr39nAZcBS4J/AZVrr\nu6a+lSJf8sZbfX8CeoCFwFHAIcB/VLVF01A6Rft5gCRWKZBS6kKcInonAvOB54HPV7VR04hSysRJ\ntrcBmI3zNzwHpyKymIBS6mKcD1Mv5dh3OHAtTgLDWcAPgVuVUm1T2UZRGOmhqCKlVBPwD+BSrXUU\niCqlrgM+Xd2WTS9KKQP4OfB94JtVbs509AXgC1rrV9Lff66ajZmG2nACsRu01kkgrJT6PXBRdZtV\n86LAMcCPAV/Wvo8Ct2utRwpS3aSU+jRwLnDV1DVRFEICiirSWvcBH8vavIi9V1AVY30C583pJiSg\nKEj6E99SoFUp9RwwF7gH+Dfprs9bB/AETs2hrwKNwFk4vY9iAlrrnwAopXLtXgn8OWvbRuDoCjdL\nlECGPGqIUuoo4FPITTFvSqm5wNeBf6tyU6arBemvZwEnAYelt11dtRZNM+kihWfh1BvqB3bgvLde\nVs12TXOtQDhrWw/O8IeoURJQ1Ail1PE4EzK/pLW+p9rtmUa+D1yjtf5ntRsyTRnpr9/VWr+eLsb3\nNeBdSilvFds1baSv05+AW4AmoB0nsLipmu2agQxkjlRNkyGPGqCUeidwA3Ch1vrGardnulBKvQ1Y\nDfxrepMxyeEit53pr30Z27biXMs5wPapbtA09DZgidZ6pEdiQCn1NeBJpVSz1rq3im2brroY3xvR\nkt4uapT0UFSZUmo1zrKp90gwUbAP4Nz0timluoDHAUMptUtNMDArxtmO82n68IxtS4EE0FmVFk0/\nLsBMr/YYUYd8mi7FYzjzKDIdDTxShbaIPEkPRRUppVzAL4Eva63/Vu32TEOfBy7P+H4hTg6FNzJ+\n/FXkoLVOKaWuAb6ilPo7EAH+HfhfrbVV3dZNGxuAAeAKpdS3gAac+RP3Se9E0X4JPKqUOhW4G+fD\nw/44PbmiRhm2LUF0tSilTgDuA2KMjg+OfD1Qa/1aFZs37SilFgObtdauardlOknPAfg+Ti4KN/B/\nwKe11kNVbdg0opQ6AucavhHn7/lenKW4Oyd73L5MKRXFea/zpDclAVtr3ZDefwbwXZyVb88Dn9Fa\nP1iNtor8SEAhhBBCiJLJHAohhBBClEwCCiGEEEKUTAIKIYQQQpRMAgohhBBClEwCCiGEEEKUTAIK\nIYQQQpRMAgohhBBClEwCCiGEEEKUTAIKIYQQQpRMankIUQVKqWuBNVrr+dVuywil1P/gtKmtAuf+\nBPAznKqc2yr1XEqpNwP3AGu11utz7F8PHKC1XjLJOR4D3Frrwyc6Juv4jwFXA/tprTcX1XAhZgDp\noRCiOmxqrxrlZ4AVI98opT6slLqnTOfOfr1jnqvMJruuvwIWKqVOyrVTKXUIcCROcapCnq/WfpZC\nTDnpoRBCAKC1jmRtOo4K3ShzPFc5GZPsuw2nEu2HcKpYZvsQMAzcWPZWCTHDSUAhRA1TSr0T+ApO\nFUsL2AhcMVLuPqOL/63ABcCpQAq4Hfg3rXU0fVwb8AvgJGAQuA54FriW0WGIa4FTtNZt6Z6JN6cf\nmwI+DLxKjuEEpdS9gFdrvTr9/XycIYCTgCHgZuCVrNd1LRlDPkqpLTg3+5eALwJzgeeAT2mt/5Hx\nuM8B/wrsh1Nq/THgYq31M/lcT611XCl1A/BRpZRfaz2QcW4Tp+Lq70bKjiulXMAV6e0LgG7gfuCL\nE1UDTp//zVrrhRnblgMvAx/TWv86ve1A4DvA0UALzs/j61rrv+TzWoSoNTLkIUSNUkqdDPwBeAI4\nClgFdALrlFLZ4/vfB+7C6a6/BDgf+HTG/v8DjgHeixMoNAGXM7YHIvP/78YJXjYA84BbchyT63Gk\nj10JnAmsBnYCF+d4TPbj1qRf4zuBt+DcZK8b2amUOi/9On8MLEsfkwJuV0r5crRrIr8CGgGVtX0t\nzmu9JmPbV4GLgM8DS4F/AZYDepLz73UIRCk1C/g70A6cBRwBPAT8QSl1Qr4vRIhaIj0UQtSui4Hn\ntdafHNmQvqmeDHwSp0dixN+01tem/3+NUuoynAACpdR+wLHAhVrrdeljPq6U+gcT0FqHlVIJIK61\n7kqfByYfThh5rhOAT2T0Yvw/pdQxOIHCZII4n+CT6XNdD3w9oyfhD8AKrfXz6eM7lFL/hdMbcyjw\n+F7OP/Lank2/9g8Bv87Y9SFgs9b63oxtPwb+V2s90sPSkZ5Q+mOlVPNIT0YRPg6EgCO11tvT2z6r\nlHoLTkC4t2slRM2RgEKI2nUUWZ+EtdaJ9CqEI7OOfSTr+y6cGxY4n6htnOGBTLfnOE+pDkk/18as\n7RvY+01y40gwkdaV/hoCBnCGT96hlLoOWAzUA670Ma0FtvMa4OdKqWVa681KqWac3ocrso6LAR9W\nSr0LaAO8jL5vtgDFBhTHAC9lBBMj7mF8z4kQ04IEFELUriDQl2N7P073e6aBrO9tRnsTRm622RMh\nuyi/wATtyWcSZq7XAKOv4/vAp3Bu+n/EuQ7HAv9beDP5DfADnF6JrwLvxwlOrss67hacoZWLceZO\nDAFnA98s4jkzBYEDlVLZ18UNuJRShtZaVo6IaUUCCiFqVy/OXIdsTRT2yTiW/tqQtX1Wge3JvsGP\n8AOJ9P8HJ3iu5gKfK5cPAL/RWl85skEptaqYE2mtI0qp3wLn4gQU5wF3aK13ZJw7hDPJ9Uqt9c8y\ntu/tfTMzmBvhz/o+jDMB9R05jkWCCTEdyaRMIWrXIzjzEfZQStXhDIU8mrF5bzefl3FuWsdkbX93\nHm3IvNmNBDGzM9rTDByUccwL6cdk3+hPzOO59sYL7M7a9sH018x25nszvgZYrJQ6A6enIzv3hCf9\ndc9zpoOJc3I8Z6ZeoEUplbk/ewnuw8AioE9rvXnkH84k09fzbL8QNUV6KISoHlMpNTfH9mGtdR9w\nFfA3pdRPcSYH1gNfB3zAf2UcP+lESa3100qpF4FLlFIvAR04qxb29oEiDLxRKbUS5yb3SnrbhUqp\nJ3BuuN/AWcUx8lwvKqUeBy5VSv0T2I4znPCGvTxXPh4C3q2UuglnCOVLwCbg7cDxSqmH08dNej0y\n2vpA+nr8NP0abs/av0sptRlnDsU9OAHNt3DmORwAvFkplevm/yhwIXCZUupGnBUc52Ydcw3wOeC3\nSqnLcVbvHIfzc70GZ2KmENOK9FAIUT2zcW4k2f/+B0BrfT/wLpwlmBtxxvAbgLdorV/KOM9En8gz\nt58BbAH+jLO89DWcvBTgJHLK9Zgf4Nyc/w6cpbUewhl2CAL/wJlfcFP6/5nOAp4H/gQ8CMwB/n0v\n7csn2+QncYKavwF3AC9qrS8Efo8zx+GzOc67N7/GWSp6rdbayrH//enzPYqTT+M36ed5BPgJztLY\nbDfhBCmfBp7BWY2TuSIHrXU38CacAO3POMMfVwJXaa0lmBDTkmHbMlQnxEynlKoHfJnLHJVS/wl8\nVGsdmPiRQgiRHxnyEGLfsB6Yly5ktQUn4dRHcTJaCiFEySSgEGLf8G7gezjd8c04Qx7/AXy7mo0S\nQswcMuQhhBBCiJLJpEwhhBBClEwCCiGEEEKUTAIKIYQQQpRMAgohhBBClEwCCiGEEEKUTAIKIYQQ\nQpRMAgohhBBClEwCCiGEEEKU7P8DrfPPzytHBXUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fce34978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_signf_progs(time_dfs, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Progression Scores; Z-score Normalization\n",
    "\n",
    "Here the z-score normalization procedure is applied prior to the comparison."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "progcol = datacol.create_progression_tables(feats, \"TIME\", \"ENTITY\", \"z-score\", 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Progression Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>p-value</th>\n",
       "      <th>cor_p-value</th>\n",
       "      <th>signf</th>\n",
       "      <th>Dataset1conf.</th>\n",
       "      <th>Dataset2conf.</th>\n",
       "      <th>Dataset3conf.</th>\n",
       "      <th>Dataset1</th>\n",
       "      <th>Dataset2</th>\n",
       "      <th>Dataset3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>features</th>\n",
       "      <th></th>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">FEAT1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>8.587101e-01</td>\n",
       "      <td>9.980164e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.88, -0.21]</td>\n",
       "      <td>[-1.04, -0.34]</td>\n",
       "      <td>[-1.07, -0.11]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>8.288823e-01</td>\n",
       "      <td>9.980164e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.88, -0.21]</td>\n",
       "      <td>[-1.04, -0.34]</td>\n",
       "      <td>[-1.07, -0.11]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>7.021697e-01</td>\n",
       "      <td>9.980164e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.88, -0.21]</td>\n",
       "      <td>[-1.04, -0.34]</td>\n",
       "      <td>[-1.07, -0.11]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>2.118956e-02</td>\n",
       "      <td>1.205897e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.33, -0.7]</td>\n",
       "      <td>[-1.86, -1.1]</td>\n",
       "      <td>[-1.74, -0.81]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.480527e-01</td>\n",
       "      <td>4.731937e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.33, -0.7]</td>\n",
       "      <td>[-1.86, -1.1]</td>\n",
       "      <td>[-1.74, -0.81]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>4.884718e-01</td>\n",
       "      <td>7.383389e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.33, -0.7]</td>\n",
       "      <td>[-1.86, -1.1]</td>\n",
       "      <td>[-1.74, -0.81]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.987259e-01</td>\n",
       "      <td>9.983588e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.71, -1.08]</td>\n",
       "      <td>[-1.79, -0.93]</td>\n",
       "      <td>[-1.76, -0.82]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.867351e-01</td>\n",
       "      <td>9.983588e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.71, -1.08]</td>\n",
       "      <td>[-1.79, -0.93]</td>\n",
       "      <td>[-1.76, -0.82]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>8.548892e-01</td>\n",
       "      <td>9.983588e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.71, -1.08]</td>\n",
       "      <td>[-1.79, -0.93]</td>\n",
       "      <td>[-1.76, -0.82]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.467447e-02</td>\n",
       "      <td>1.908244e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-2.2, -1.72]</td>\n",
       "      <td>[-1.81, -1.08]</td>\n",
       "      <td>[-1.33, -0.27]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>6.807820e-04</td>\n",
       "      <td>6.110379e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[-2.2, -1.72]</td>\n",
       "      <td>[-1.81, -1.08]</td>\n",
       "      <td>[-1.33, -0.27]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>4.085755e-02</td>\n",
       "      <td>1.908244e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-2.2, -1.72]</td>\n",
       "      <td>[-1.81, -1.08]</td>\n",
       "      <td>[-1.33, -0.27]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>6.129396e-05</td>\n",
       "      <td>3.677074e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>[-2.59, -2.15]</td>\n",
       "      <td>[-1.82, -1.09]</td>\n",
       "      <td>[-0.89, 0.21]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.258812e-08</td>\n",
       "      <td>8.811686e-08</td>\n",
       "      <td>True</td>\n",
       "      <td>[-2.59, -2.15]</td>\n",
       "      <td>[-1.82, -1.09]</td>\n",
       "      <td>[-0.89, 0.21]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.740987e-03</td>\n",
       "      <td>8.674678e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[-2.59, -2.15]</td>\n",
       "      <td>[-1.82, -1.09]</td>\n",
       "      <td>[-0.89, 0.21]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">LONG1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.952027e-06</td>\n",
       "      <td>1.756810e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.23, 0.64]</td>\n",
       "      <td>[-0.51, -0.11]</td>\n",
       "      <td>[0.28, 0.71]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>6.455329e-01</td>\n",
       "      <td>9.980164e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.23, 0.64]</td>\n",
       "      <td>[-0.51, -0.11]</td>\n",
       "      <td>[0.28, 0.71]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>2.276022e-06</td>\n",
       "      <td>1.820803e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.23, 0.64]</td>\n",
       "      <td>[-0.51, -0.11]</td>\n",
       "      <td>[0.28, 0.71]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.208919e-05</td>\n",
       "      <td>6.486157e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>[1.01, 1.25]</td>\n",
       "      <td>[0.48, 0.81]</td>\n",
       "      <td>[0.74, 1.21]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>2.513110e-01</td>\n",
       "      <td>5.803334e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[1.01, 1.25]</td>\n",
       "      <td>[0.48, 0.81]</td>\n",
       "      <td>[0.74, 1.21]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>2.779769e-02</td>\n",
       "      <td>1.314731e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[1.01, 1.25]</td>\n",
       "      <td>[0.48, 0.81]</td>\n",
       "      <td>[0.74, 1.21]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>8.241152e-02</td>\n",
       "      <td>4.974450e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[1.52, 1.8]</td>\n",
       "      <td>[1.24, 1.62]</td>\n",
       "      <td>[1.48, 1.87]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.249407e-01</td>\n",
       "      <td>9.983588e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[1.52, 1.8]</td>\n",
       "      <td>[1.24, 1.62]</td>\n",
       "      <td>[1.48, 1.87]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.676081e-01</td>\n",
       "      <td>7.231179e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[1.52, 1.8]</td>\n",
       "      <td>[1.24, 1.62]</td>\n",
       "      <td>[1.48, 1.87]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>2.118956e-02</td>\n",
       "      <td>1.392240e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[1.99, 2.36]</td>\n",
       "      <td>[2.34, 2.58]</td>\n",
       "      <td>[1.98, 2.33]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>5.832477e-01</td>\n",
       "      <td>6.591306e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[1.99, 2.36]</td>\n",
       "      <td>[2.34, 2.58]</td>\n",
       "      <td>[1.98, 2.33]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>3.885339e-03</td>\n",
       "      <td>3.066329e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>[1.99, 2.36]</td>\n",
       "      <td>[2.34, 2.58]</td>\n",
       "      <td>[1.98, 2.33]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.415204e-13</td>\n",
       "      <td>1.273981e-12</td>\n",
       "      <td>True</td>\n",
       "      <td>[2.64, 2.83]</td>\n",
       "      <td>[1.22, 1.65]</td>\n",
       "      <td>[2.57, 2.84]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.955782e-01</td>\n",
       "      <td>9.955782e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[2.64, 2.83]</td>\n",
       "      <td>[1.22, 1.65]</td>\n",
       "      <td>[2.57, 2.84]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.657495e-11</td>\n",
       "      <td>1.325997e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>[2.64, 2.83]</td>\n",
       "      <td>[1.22, 1.65]</td>\n",
       "      <td>[2.57, 2.84]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">NDSIG1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.838991e-01</td>\n",
       "      <td>9.980164e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.66, 0.31]</td>\n",
       "      <td>[-0.79, 0.34]</td>\n",
       "      <td>[-0.59, 0.55]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>7.353216e-01</td>\n",
       "      <td>9.980164e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.66, 0.31]</td>\n",
       "      <td>[-0.79, 0.34]</td>\n",
       "      <td>[-0.59, 0.55]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>5.756631e-01</td>\n",
       "      <td>9.975227e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.66, 0.31]</td>\n",
       "      <td>[-0.79, 0.34]</td>\n",
       "      <td>[-0.59, 0.55]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.153999e-02</td>\n",
       "      <td>7.803649e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.3, -0.31]</td>\n",
       "      <td>[-0.4, 0.72]</td>\n",
       "      <td>[-0.3, 0.82]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>4.790087e-03</td>\n",
       "      <td>3.768435e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>[-1.3, -0.31]</td>\n",
       "      <td>[-0.4, 0.72]</td>\n",
       "      <td>[-0.3, 0.82]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>9.513904e-01</td>\n",
       "      <td>9.513904e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.3, -0.31]</td>\n",
       "      <td>[-0.4, 0.72]</td>\n",
       "      <td>[-0.3, 0.82]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.955871e-01</td>\n",
       "      <td>7.290593e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.82, 0.14]</td>\n",
       "      <td>[-0.37, 0.71]</td>\n",
       "      <td>[-0.16, 0.85]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>4.914051e-02</td>\n",
       "      <td>3.646002e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.82, 0.14]</td>\n",
       "      <td>[-0.37, 0.71]</td>\n",
       "      <td>[-0.16, 0.85]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>7.186805e-01</td>\n",
       "      <td>9.982380e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.82, 0.14]</td>\n",
       "      <td>[-0.37, 0.71]</td>\n",
       "      <td>[-0.16, 0.85]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>4.161598e-01</td>\n",
       "      <td>6.591306e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.0, 0.11]</td>\n",
       "      <td>[-0.67, 0.33]</td>\n",
       "      <td>[-0.29, 0.75]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>5.878685e-02</td>\n",
       "      <td>2.152127e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.0, 0.11]</td>\n",
       "      <td>[-0.67, 0.33]</td>\n",
       "      <td>[-0.29, 0.75]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>2.949073e-01</td>\n",
       "      <td>6.494591e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-1.0, 0.11]</td>\n",
       "      <td>[-0.67, 0.33]</td>\n",
       "      <td>[-0.29, 0.75]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.254222e-01</td>\n",
       "      <td>9.855865e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.63, 0.36]</td>\n",
       "      <td>[-0.47, 0.47]</td>\n",
       "      <td>[-0.55, 0.47]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>6.535089e-01</td>\n",
       "      <td>9.855865e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.63, 0.36]</td>\n",
       "      <td>[-0.47, 0.47]</td>\n",
       "      <td>[-0.55, 0.47]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>8.898080e-01</td>\n",
       "      <td>9.878577e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.63, 0.36]</td>\n",
       "      <td>[-0.47, 0.47]</td>\n",
       "      <td>[-0.55, 0.47]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           p-value   cor_p-value  signf   Dataset1conf.  \\\n",
       "features    datasets                                                      \n",
       "FEAT1    2  (1, 2)    8.587101e-01  9.980164e-01  False  [-0.88, -0.21]   \n",
       "            (1, 3)    8.288823e-01  9.980164e-01  False  [-0.88, -0.21]   \n",
       "            (2, 3)    7.021697e-01  9.980164e-01  False  [-0.88, -0.21]   \n",
       "         4  (1, 2)    2.118956e-02  1.205897e-01  False   [-1.33, -0.7]   \n",
       "            (1, 3)    1.480527e-01  4.731937e-01  False   [-1.33, -0.7]   \n",
       "            (2, 3)    4.884718e-01  7.383389e-01  False   [-1.33, -0.7]   \n",
       "         6  (1, 2)    7.987259e-01  9.983588e-01  False  [-1.71, -1.08]   \n",
       "            (1, 3)    9.867351e-01  9.983588e-01  False  [-1.71, -1.08]   \n",
       "            (2, 3)    8.548892e-01  9.983588e-01  False  [-1.71, -1.08]   \n",
       "         8  (1, 2)    3.467447e-02  1.908244e-01  False   [-2.2, -1.72]   \n",
       "            (1, 3)    6.807820e-04  6.110379e-03   True   [-2.2, -1.72]   \n",
       "            (2, 3)    4.085755e-02  1.908244e-01  False   [-2.2, -1.72]   \n",
       "         10 (1, 2)    6.129396e-05  3.677074e-04   True  [-2.59, -2.15]   \n",
       "            (1, 3)    1.258812e-08  8.811686e-08   True  [-2.59, -2.15]   \n",
       "            (2, 3)    1.740987e-03  8.674678e-03   True  [-2.59, -2.15]   \n",
       "LONG1    2  (1, 2)    1.952027e-06  1.756810e-05   True    [0.23, 0.64]   \n",
       "            (1, 3)    6.455329e-01  9.980164e-01  False    [0.23, 0.64]   \n",
       "            (2, 3)    2.276022e-06  1.820803e-05   True    [0.23, 0.64]   \n",
       "         4  (1, 2)    7.208919e-05  6.486157e-04   True    [1.01, 1.25]   \n",
       "            (1, 3)    2.513110e-01  5.803334e-01  False    [1.01, 1.25]   \n",
       "            (2, 3)    2.779769e-02  1.314731e-01  False    [1.01, 1.25]   \n",
       "         6  (1, 2)    8.241152e-02  4.974450e-01  False     [1.52, 1.8]   \n",
       "            (1, 3)    9.249407e-01  9.983588e-01  False     [1.52, 1.8]   \n",
       "            (2, 3)    1.676081e-01  7.231179e-01  False     [1.52, 1.8]   \n",
       "         8  (1, 2)    2.118956e-02  1.392240e-01  False    [1.99, 2.36]   \n",
       "            (1, 3)    5.832477e-01  6.591306e-01  False    [1.99, 2.36]   \n",
       "            (2, 3)    3.885339e-03  3.066329e-02   True    [1.99, 2.36]   \n",
       "         10 (1, 2)    1.415204e-13  1.273981e-12   True    [2.64, 2.83]   \n",
       "            (1, 3)    9.955782e-01  9.955782e-01  False    [2.64, 2.83]   \n",
       "            (2, 3)    1.657495e-11  1.325997e-10   True    [2.64, 2.83]   \n",
       "NDSIG1   2  (1, 2)    7.838991e-01  9.980164e-01  False   [-0.66, 0.31]   \n",
       "            (1, 3)    7.353216e-01  9.980164e-01  False   [-0.66, 0.31]   \n",
       "            (2, 3)    5.756631e-01  9.975227e-01  False   [-0.66, 0.31]   \n",
       "         4  (1, 2)    1.153999e-02  7.803649e-02  False   [-1.3, -0.31]   \n",
       "            (1, 3)    4.790087e-03  3.768435e-02   True   [-1.3, -0.31]   \n",
       "            (2, 3)    9.513904e-01  9.513904e-01  False   [-1.3, -0.31]   \n",
       "         6  (1, 2)    1.955871e-01  7.290593e-01  False   [-0.82, 0.14]   \n",
       "            (1, 3)    4.914051e-02  3.646002e-01  False   [-0.82, 0.14]   \n",
       "            (2, 3)    7.186805e-01  9.982380e-01  False   [-0.82, 0.14]   \n",
       "         8  (1, 2)    4.161598e-01  6.591306e-01  False    [-1.0, 0.11]   \n",
       "            (1, 3)    5.878685e-02  2.152127e-01  False    [-1.0, 0.11]   \n",
       "            (2, 3)    2.949073e-01  6.494591e-01  False    [-1.0, 0.11]   \n",
       "         10 (1, 2)    7.254222e-01  9.855865e-01  False   [-0.63, 0.36]   \n",
       "            (1, 3)    6.535089e-01  9.855865e-01  False   [-0.63, 0.36]   \n",
       "            (2, 3)    8.898080e-01  9.878577e-01  False   [-0.63, 0.36]   \n",
       "\n",
       "                       Dataset2conf.   Dataset3conf.  Dataset1  Dataset2  \\\n",
       "features    datasets                                                       \n",
       "FEAT1    2  (1, 2)    [-1.04, -0.34]  [-1.07, -0.11]        40        40   \n",
       "            (1, 3)    [-1.04, -0.34]  [-1.07, -0.11]        40        40   \n",
       "            (2, 3)    [-1.04, -0.34]  [-1.07, -0.11]        40        40   \n",
       "         4  (1, 2)     [-1.86, -1.1]  [-1.74, -0.81]        40        40   \n",
       "            (1, 3)     [-1.86, -1.1]  [-1.74, -0.81]        40        40   \n",
       "            (2, 3)     [-1.86, -1.1]  [-1.74, -0.81]        40        40   \n",
       "         6  (1, 2)    [-1.79, -0.93]  [-1.76, -0.82]        40        40   \n",
       "            (1, 3)    [-1.79, -0.93]  [-1.76, -0.82]        40        40   \n",
       "            (2, 3)    [-1.79, -0.93]  [-1.76, -0.82]        40        40   \n",
       "         8  (1, 2)    [-1.81, -1.08]  [-1.33, -0.27]        40        40   \n",
       "            (1, 3)    [-1.81, -1.08]  [-1.33, -0.27]        40        40   \n",
       "            (2, 3)    [-1.81, -1.08]  [-1.33, -0.27]        40        40   \n",
       "         10 (1, 2)    [-1.82, -1.09]   [-0.89, 0.21]        40        40   \n",
       "            (1, 3)    [-1.82, -1.09]   [-0.89, 0.21]        40        40   \n",
       "            (2, 3)    [-1.82, -1.09]   [-0.89, 0.21]        40        40   \n",
       "LONG1    2  (1, 2)    [-0.51, -0.11]    [0.28, 0.71]        40        40   \n",
       "            (1, 3)    [-0.51, -0.11]    [0.28, 0.71]        40        40   \n",
       "            (2, 3)    [-0.51, -0.11]    [0.28, 0.71]        40        40   \n",
       "         4  (1, 2)      [0.48, 0.81]    [0.74, 1.21]        40        40   \n",
       "            (1, 3)      [0.48, 0.81]    [0.74, 1.21]        40        40   \n",
       "            (2, 3)      [0.48, 0.81]    [0.74, 1.21]        40        40   \n",
       "         6  (1, 2)      [1.24, 1.62]    [1.48, 1.87]        40        40   \n",
       "            (1, 3)      [1.24, 1.62]    [1.48, 1.87]        40        40   \n",
       "            (2, 3)      [1.24, 1.62]    [1.48, 1.87]        40        40   \n",
       "         8  (1, 2)      [2.34, 2.58]    [1.98, 2.33]        40        40   \n",
       "            (1, 3)      [2.34, 2.58]    [1.98, 2.33]        40        40   \n",
       "            (2, 3)      [2.34, 2.58]    [1.98, 2.33]        40        40   \n",
       "         10 (1, 2)      [1.22, 1.65]    [2.57, 2.84]        40        40   \n",
       "            (1, 3)      [1.22, 1.65]    [2.57, 2.84]        40        40   \n",
       "            (2, 3)      [1.22, 1.65]    [2.57, 2.84]        40        40   \n",
       "NDSIG1   2  (1, 2)     [-0.79, 0.34]   [-0.59, 0.55]        40        40   \n",
       "            (1, 3)     [-0.79, 0.34]   [-0.59, 0.55]        40        40   \n",
       "            (2, 3)     [-0.79, 0.34]   [-0.59, 0.55]        40        40   \n",
       "         4  (1, 2)      [-0.4, 0.72]    [-0.3, 0.82]        40        40   \n",
       "            (1, 3)      [-0.4, 0.72]    [-0.3, 0.82]        40        40   \n",
       "            (2, 3)      [-0.4, 0.72]    [-0.3, 0.82]        40        40   \n",
       "         6  (1, 2)     [-0.37, 0.71]   [-0.16, 0.85]        40        40   \n",
       "            (1, 3)     [-0.37, 0.71]   [-0.16, 0.85]        40        40   \n",
       "            (2, 3)     [-0.37, 0.71]   [-0.16, 0.85]        40        40   \n",
       "         8  (1, 2)     [-0.67, 0.33]   [-0.29, 0.75]        40        40   \n",
       "            (1, 3)     [-0.67, 0.33]   [-0.29, 0.75]        40        40   \n",
       "            (2, 3)     [-0.67, 0.33]   [-0.29, 0.75]        40        40   \n",
       "         10 (1, 2)     [-0.47, 0.47]   [-0.55, 0.47]        40        40   \n",
       "            (1, 3)     [-0.47, 0.47]   [-0.55, 0.47]        40        40   \n",
       "            (2, 3)     [-0.47, 0.47]   [-0.55, 0.47]        40        40   \n",
       "\n",
       "                      Dataset3  \n",
       "features    datasets            \n",
       "FEAT1    2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "LONG1    2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "NDSIG1   2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result, time_dfs = progcol.analyze_longitudinal_feats(\"TIME\", 0, include=feats)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGNCAYAAABJ++jzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fcc5e4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGNCAYAAABJ++jzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEBERkYr9/1EjNVxC9hdYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fcfe6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAhQAAAGNCAYAAABJ++jzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAPYQAAD2EBqD+naQAAIABJREFUeJzs3Xl8VNX5+PHPnS2TZCbJZCEkLAIuI3Eh0SolQUUtVKpW\nqXaU0lr9idvXqhRU7KaIuBOoWgShFq1LdVqt1CqISisiCGpBwZiRLciWhSSTTDKZ/f7+mCQQksAk\nmSST5Hm/Xnklufeee8+cTGaeec655yiqqiKEEEII0RWa3q6AEEIIIfo+CSiEEEII0WUSUAghhBCi\nyySgEEIIIUSXSUAhhBBCiC6TgEIIIYQQXSYBhRBCCCG6TAIKIYQQQnSZBBRCCCGE6DIJKIQQQgjR\nZbreroAQovdZrdZfAsuB+xwOxxMRlpkATAfygUwgBOwD/gMscjgcX7dR5gXgOmAbkOdwOIJtHHMC\nsBuY43A45rax/0Lg/x1xXT1QDmwGXnM4HK8eo86DgBeBH7Z3fiFE50hAIYRoEtHCPlar1QD8Gfg5\n4Tf+V4FiQAuc3rj9FqvV+pDD4ZjTzjVOA34NzI+0clarNQ74CzAVcAAvAdsBI3BK4/bLrFbr1cA0\nh8PRcFT5S4HngfhIH6sQInISUAghmigRHvc04aBhGXC7w+EIHLnTarU+CNiB+61Wq9PhcPzxqPIq\n4SzG/Var9W8Oh2N/hNddRDhoeAb4tcPhCB113fsJBzdXAo8QDlia9k0C3gZeazzmXxFeUwgRIRlD\nIYSImNVqPQ24GdgE3Hp0MAHgcDjqgGuAA8Bcq9Wa0sap/g+IA56K8LqnE+7mWO9wOO46OphovK6X\ncHfKbwhnMo5kbKzvzwBXJNcUQnSMBBRCiI74JeEMwwKHw9Fut4HD4XABS4BE4Oo29n8LFAJTrFbr\n5Aiue13TdY91kMPhqHc4HE84HI6tR23/l8PhWBrBdYQQnSQBhRCiI85t/P5RBMf+h3A3yrh29j8E\nfAc80zg+IpLrrovgukKIXiBjKIQQHZEJhBwOR1kEx+5r/D64rZ0Oh6PBarXOAP4J/BZ4IILrlh+9\nw2q1xgOGNsrUtXUXiRCie0iGQgjRESEiH7zZdFy7b+oOh2MF8A5wr9VqPekY5woe47pLgeo2vgoi\nrKcQIgokoBBCdMQBQLFarcMiOLbpmAPHOe4OwoHKsxFc94Q29s0DJhzx9SJyW6gQPU4CCiFER6xv\n/P6DCI69kPAb+8fHOsjhcJQAjwIXW61WWzuHNY2dmNRGeYfD4Vjb9AWURFA3IUSUSUAhhOiIpk//\nd1utVn17B1mt1kTCt5dWER4jcTxPADsI38WR1Mb+FwhnMe6OYACnEKIXSEAhhIiYw+HYBTwJnAq8\nbLVajUcfY7VaTYQntsoCZjocDncE5/UBtwPZhO/+UI/a/x3hyapOAuyN12jFarVeTDiQAWho6xgh\nRPeQuzyEEEc6w2q1XtXOvi0Oh2Mn8DvARHhyqu9brdZXgG8If0A5DZgGpBOezfKlSC/scDg+sFqt\nfwd+SttjIOYQngzrbmB743W3Es5cDAMuJXyLagXhqbc/aypotVrPBzIafz298ftpRzzWeofDsSrS\nugohWpOAQgjRRAV+1vjVll8DTzfOUnmH1Wp9GbgVsBHORgSBvcDfgWcag4/2rtOeXxNeuMt89I7G\nibTus1qtf2u87mWEsxEawkHE1sbtL7eRFXkQOP+oOlzN4Um39gCjjlEvIcRxKKoqg6GFEEII0TV9\ncgyFzWZbaLPZWs3lL4QQQoje0ecCCpvNlgv8ArnPXAghhIgZfSqgsNlsCrCY8KJCQgghhIgRfSqg\nIDzgqgF4tbcrIoQQQojD+sxdHjabLZPwbWPnH+fQtqQRHjleAniiVyshhBCi3zMCI4D3gMr2Duoz\nAQXhbo7n7Xa7w2aztTWf/7H8EHilG+okhBBCDBTTOEYPQZ8IKGw228VAPnBT46ZIVztsUgJQW1tL\nMBh7qxlrtVqSkpJitn59gbRh10kbdp20YddJG8aepr8Jx1knp08EFISjokHAdzabDcJjPxSbzVYO\n/Mput9uPU94D4PV68fv93VrRztDrw0sixGr9+gJpw66TNuw6acOukzaMPU1/E44zZKCvBBS/Bn5/\nxO/DgA3AGKC6V2okhBBCiGZ9IqCw2+01QE3T7zabTQ+odrv9YO/VSgghhBBN+kRAcTS73b4H0PZ2\nPYQQQggR1tfmoRBCCCFEDJKAQgghhBBdJgGFEEIIIbpMAgohhBBCdJkEFEIIIYToMgkohBBCCNFl\nElAIIYQQosskoBBCCCFEl0lAIYQQQoguk4BCCCGEEF3WJ6feFkII0XPu+ucOSl2+HrmWooBWqyMY\nDKCqh7cPNht4aspJnTrn448/znvvvYeiKKiqik6nIzU1lZycHC6//HLy8vKiVPvedfDgQebOnYvD\n4eDFF19k2LBhPXp9CSiEEEIcU6nLx16ntwevGP1rpaSk8Je//AVVVfH7/Rw4cIAPPviAu+++m6uv\nvprbbrst4nP5/X4uvfRSXnrpJTIzM6Ne1+NZvnw55eXlzJ49u3nbRx99xPz580lPT0dRlB6vE0hA\nIYQQYgDQaDSkpKQ0/z5o0CByc3M566yzmDdvHiNHjuSSSy6J6FzFxcUEg8HuqupxFRUVkZ6e3mLb\n4sWLufPOOwkGgzz55JO9Ui8JKIQQQgxYF110Ee+++y6vvvoql1xyCQ0NDSxbtoyPP/4Yp9OJxWLh\nnHPO4dZbb8VsNrNq1SqeeOIJFEVh6tSp5ObmsmDBgnbL3XLLLSQlJQFQV1fH4sWL2bRpEzU1NVgs\nFsaPH88tt9yCwWAAoKqqiiVLlrBt2zYqKyvJzs7GZrMxefJkAKZOnUpZWRkAq1evZsGCBYwZM4YF\nCxaQnZ3NqlWreqchkYBCCCHEAFdQUMCf/vQnKioqWL58OevXr+f+++9n2LBhlJaW8thjj1FYWMic\nOXO46KKLcDqdLFu2jCVLlpCdnQ3AM88802a5BQsWMGfOHACefvppdu7cydy5c0lPT6ekpITCwkKC\nwSAzZswgEAgwc+ZM/H4/M2fOJCsri7Vr1zJ//nx0Oh0TJ05kyZIl3HDDDeTl5XHHHXdgNpsBmuvR\nmySgEEIIMaANGjQIgMrKSqZPn851113H4MGDAcjIyGDChAm89dZbABgMBhISEgBITk7GZDIBHLcc\nwLfffktubi6jR49uPmbhwoWEQiEA1q5dy969e5uzDhDOSBQVFfHyyy8zceJEkpOTURQFg8HQogsn\nFkhAIYQQYkALBAIAaLVaFEXhjTfeYNOmTVRVVREMBgkEAgSDQfx+P3q9vs1zRFKuoKCA119/HZ/P\nR35+Pnl5eWRlZTWfo7i4GJ1Ox5lnntni3Hl5eaxfvx6Px4PRaOy+hugiCSiEEEIMaPv27QPCmYpZ\ns2ZRUVHB7bffzimnnILBYOCNN95okWloyz333HPccjfddBMjRoxg5cqVzJ07F4Bx48Zx5513kpaW\nhtvtbr6D5EhNA0ArKysZMmRINB96VElAIYQQYkBbu3YtVquVqqoqdu3axaxZs5g0aVLzfp/v2HNw\n7N69O+JyEydOZOLEiXg8HjZu3MjixYuZN28eCxcuxGQyERcXx/PPP4965CQcjXrjFtWOkJkyhRBC\nDFhvvvkmO3bsYNq0afj9fiA8NqJJfX0969atA2j1Jt/0eyTlvF4va9asoa6uDgCj0cgFF1zA1Vdf\nzfbt2wHIycnB5/PR0NBAdnZ281dcXBwmkwmtVtsdTRA1ElAIIYTo90KhEFVVVVRVVVFZWUlRURHz\n589n0aJFTJs2jYKCAoYPH47ZbGbFihXs27ePoqIiZs+ezfjx4wHYvHkzXq8Xs9mMqqps2LCB3bt3\nH7fcli1bCAQCLF26lEcffZTi4mIqKirYtm0b77//Prm5uQDk5+czYsQIHn74Yb744gvKysrYuHEj\nd911F4WFhc2PxWw2s337dnbs2EF1dTWBQKD5sdXV1aGqKk6nk6qqKmpqanqsjZW20ir90FnAFxUV\nFc2RZCzR6/VkZGQQq/XrC6QNu07asOv6axv2h6m3V69e3WJbUlISo0eP5qqrruLss89u3v7ZZ5+x\naNEiysrKyM7OZvr06eTk5DBjxgxKS0uZN28eOTk53HfffXzzzTeMGjWKJUuWsGnTJp599tljlsvM\nzOS5555j27ZtuN1uUlNTGTt2LDfeeGPz7Z81NTU899xzfPrpp7hcLlJTU7n44ou5/vrrm+eqWL16\nNYsWLcLv9zN79mySk5OZOXNmmzNkZmZm8uqrr3aq3Zo0Pa+Bs4H/tXecBBQxoL++CPUkacOukzbs\nOmnDrpM2jD2RBhTS5SGEEEKILpOAQgghhBBdJgGFEEIIIbpMAgohhBBCdJlMbCWEEKL/cZdDwN16\nuy4BEgb1fH0GAAkohBBCdMj+Gi913mCr7aY4LUOS43qhRkfxudCsn41C67sYVUVD6LynwGDuhYr1\nbxJQCCGEiJizIcA1fy0i1MaMA1oF3p5+BinxvfzWYjATyn88nKGoP4D266UET7sZErPDGQoJJrqF\nBBRCCCEilhKv4/XrcqjzBimp8vDg6j08MOkERqQaMcVpez+YaHJ0t0ZiNiSN6JWqDBQx8pcXQgjR\nVxzdrTEi1Yh1UEIv1UbECgkohBADSnv9/xBDYwCE6IMkoBBCDBjH6v+HGBoDEGNu21vNQX+o1XZ/\nMIRn/CBm1tSjr2uIyrUUBbQ7KwgGgy3W8sjSa1g8zNKpcz769iFWPfL/UBQFVVXR6XSkpqaSk5PD\n5ZdfTl5eXlTq3pvKyspYunQpW7duxe12M2zYMKZOncr555/fY3WQ/xohxIBxZP9/W2JqDEAMOegP\n8Z2/7TYjUc/BkAqhdvZ3ShTPpdGj6hJISdbxl+UvoKoqfr+fAwcO8MEHH3D33Xdz9dVXc9ttt0V8\nSr/fz6WXXspLL71EZmZm9OoaoeXLl1NeXs7s2bMBqKur46677iI1NZU5c+aQlJTEm2++yZw5c3jy\nySdbLH7WneQ/RwgxoEiXxgBjGgKDzkaz5zNSUlKaNw8aNIjc3FzOOuss5s2bx8iRI7nkkksiOmVx\ncTHBYDQDqI4pKioiPT29+fc1a9ZQUVHB008/zaBB4cGod9xxB5988gkrVqyQgEIIIYTobhdddBHv\nvvsur776KpdccgkNDQ0sW7aMjz/+GKfTicVi4ZxzzuHWW2/FbDazatUqnnjiCRRFYerUqeTm5rJg\nwYJ2y91yyy0kJSUB4UzC4sWL2bRpEzU1NVgsFsaPH88tt9zSvDR5VVUVS5YsYdu2bVRWVpKdnY3N\nZmPy5MkATJ06lbKyMiC8jPmCBQu49NJLKSgoIC0trflxKYpCamoqDQ3R6YqKhAQUQgghBrSCggL+\n9Kc/UVFRwfLly1m/fj33338/w4YNo7S0lMcee4zCwkLmzJnDRRddhNPpZNmyZSxZsoTs7GwAnnnm\nmTbLLViwgDlz5gDw9NNPs3PnTubOnUt6ejolJSUUFhYSDAaZMWMGgUCAmTNn4vf7mTlzJllZWaxd\nu5b58+ej0+mYOHEiS5Ys4YYbbiAvL4877rgDs9mMVqttEUxAeEzFzp07+dnPftZj7SgBhRBCiAGt\nqZugsrKS6dOnc9111zF48GAAMjIymDBhAm+99RYABoOBhITwLbLJycmYTCaA45YD+Pbbb8nNzWX0\n6NHNxyxcuJBQKDzgde3atezdu5cFCxYwZswYIJyRKCoq4uWXX2bixIkkJyejKAoGg6FFF86RfD4f\n8+bNIzk5mauuuiqqbXUsElAIIYQY0AKBAABarRZFUXjjjTfYtGkTVVVVBINBAoEAwWAQv9+PXq9v\n8xyRlCsoKOD111/H5/ORn59PXl4eWVlZzecoLi5Gp9Nx5plntjh3Xl4e69evx+PxYDQaj/lY3G43\nv/3tb9mzZw+FhYWYzT03K6gEFEII0UX7fEFcoRC6gIrF5aa6wUcgEMCs0TDUoO3t6onj2LdvHxDO\nVMyaNYuKigpuv/12TjnlFAwGA2+88UaLTENb7rnnnuOWu+mmmxgxYgQrV65k7ty5AIwbN44777yT\ntLQ03G538x0kR2oaAFpZWcmQIUParUNVVRX33nsvtbW1PPXUU4wcObJT7dFZfSagsNlsY4BC4HtA\nA/ARcJfdbi/r1YoJIfqkQ/V+3tp6iCvPSCc9se1PnZGoDoS4cnclh2dpqGj+SQusPjEdi07TlaqK\nbrZ27VqsVitVVVXs2rWLWbNmMWnSpOb9Pp/vmOV3794dcbmJEycyceJEPB4PGzduZPHixcybN4+F\nCxdiMpmIi4vj+eefR1VbT5ZyrFtUXS4XM2fORFEUFi1aREZGRiQPPar6xLPcZrMZgPeANUAGcDqQ\nCTzbm/USQvRdlfV+/rKplMp6f5fOY9FpeGtkGq+cYKFwqIXhRgOFQy28coKFf45Mk2Aixr355pvs\n2LGDadOm4feHnwvJycnN++vr61m3bh1Aqzf5pt8jKef1elmzZg11dXUAGI1GLrjgAq6++mq2b98O\nQE5ODj6fj4aGBrKzs5u/4uLiMJlMaLXtZ7seeOABQqEQf/zjH3slmIC+k6FIAH4LvGC320NApc1m\nexP4Ve9WSwghaOzW0HKmXs+0k0+goqKi+U1G9LK6/VD+BaFggKqqKiD8Bl9WVsa7777LypUrmTZt\nGgUFBXg8HsxmMytWrGDEiBHU1tby7LPPMn78eN599102b95Mbm4uZrMZVVXZsGEDubm5DB8+/Jjl\ntmzZwmmnncbSpUv58MMP+cUvfkFaWhplZWW8//775ObmApCfn8+IESN4+OGH+b//+z+GDh1KSUkJ\nzzzzDCeeeCIPPvggAGazme3bt7Njxw7S0tLYvHkzW7ZsYd68eQSDwebH2SQ1NbVHmlppK60S62w2\nmxV4FXjbbrfPiaDIWcAXsfpPrtfrycjIkBehLpA27LqB1oaOcjc3vOZg+bXWqC1sFbU2dJeHl94+\nmi6h9SqaPeDKXZXtz5TZQ4brtbw1Ku34Bx6ttoQn/vAr3tvmBpTmzUlJSYwePZqrrrqqxcRPn332\nGYsWLaKsrIzs7GymT59OTk4OM2bMoLS0lHnz5pGTk8N9993HN998w6hRo1iyZAmbNm3i2WefPWa5\nzMxMnnvuObZt24bb7SY1NZWxY8dy4403Ng+erKmp4bnnnuPTTz/F5XKRmprKxRdfzPXXX988V8Xq\n1atZtGgRfr+f2bNn8+mnn7J69epWD11VVRRF4cMPP+x4ux2h6XkNnA38r73j+lRAYbPZhgPbCXdN\nLgVut9vtkTyAs4Avqqurm0fzxhKdTofFYiFW69cXSBt23UBrw+Kyen7x8te89PPTODUzMSrnjEYb\nqj4X6oe3AW28tCkalIueRTH03Mh9gJt3H+KAv/Xj8QVVDtZ6yUqKw6BV2ijZcQoKGq2GUDCEekQb\nZOt1LB2ZfoySbVNrdqOu/z1K/jyU5J4dpNhfND2vOU5A0Ve6PACw2+3fAXE2m+1EwgHFy8C0SMs3\nNkjMivX69QXShl03UNqwzBf+tGexWMjISD7O0R3TtTbMIDDlBVRfXas9isGEzpzVRpnu9c92+uS3\n7a/hsmfW8dId4zl9SHTbMFr8GidVhP8m+rTeGVswUPSpgKKJ3W7fabPZfgest9lsd9rt9spIysXq\nJ6+B9smwO0gbdt1Aa8Pq6vrG79VUGI49ij9S0WtDHdDGpEUewFPRensvie02DFNrqoFwHZVQ7LRd\nX3JEhuLYx/VAXbrMZrNdCCy22+2nHrFZbfyKuKMyEAjEdN9wrNevL5A27LqB0oZNb1bd8XilDaNz\n7qicMxBA23g+BsDfpDf1iYAC+AJIstlsjwEPAibgAWCt3W6v7dWaCSH6JINWYWSqMWp9/0IMdH3i\nBunGoGEiMJbwrDFbgWqg51Y9EUL0KyPT4nnl56MZmRbf21Vpn9eJsvOf4HX2dk36Bk8l1Ja0/Ko/\nEN5Xf6D1Pk9EveUiQn0lQ4Hdbv8auLC36yGEEO3Z6fFz1cZinshOYng0Ztz2OtHsXkEwIw/i2l4I\nqqeUunzUNLQc01BS5Wnx/UjJ8ToGmw09UjcAPJVo1t+HEmq7W0P79dJW21SNnlD+Y2DsxO2oopU+\nE1AIIUSs86oq37o9eFUzR8550NeVunxc+9cifMG279J/cPWeVtsMWoXXrsvpuaDC52o3mGiPEvKD\nzyUBRZT0iS4PIYQQvaemIdBuMNEeX1BtldEQ/ZsEFEIIIYToMunyEEJEV4xNGy0GDm25G8UdeVZE\nTdDRuxOK9y8SUAghosfnQrN+Nkob00ariobQeU9BD08bLbpu+K6nsZ9Y2uFyg4sNaHZ0LBEeAg5p\ntYSCwRYpdNWYhnrWPe2W01Q7Sbv3E5QO9MyoGoWyAiehpA5VUbRDAgohRPQYzITyHw9nKEJ+8FZD\nnAU0+nCGQoKJPknvq+KEuE7cuupr/OqgpqzB0cNajxUrhCwpVD5R0GaGYt6Gelbu8qEooKqg14DF\nqOH0wXou3XuQvBNyO17JGLN7925eeOEFtm7dSl1dHUOHDmXKlClcfvnlPVYHCSiEENHV1K1RW4J2\n67MEz50DSSN6s0Zt2l3ZwO9XljBv8ohOzUVx0B/EGQy12PZd48DFXV4/gUDLt8MUrYYsfQfvJdXo\nUROzwwFZL1JCfaNjINjOqrHqVh+WxAAv3JSNSnjA6IHqAB9sq+fuB+Zz9dUl3HbbbRFfx+/3c+ml\nl/LSSy+RmZkZpdpHbvny5ZSXlzN79mwASktLmTFjBmPGjOHJJ5/EaDTy9ttvs3DhQrRaLT/60Y96\npF4SUAghBiRfUGV3lafDdy9AOJiYsrsSXztFf7Ov9ad5gwL/HJnWsaDCNITQuEc6XL9oUzXRmFSj\ndymKQkri4ceRmaQj7wQjuRf/nHkLnmPkyJFccsklEZ2ruLiYYLD3gqyioiLS0w+vvLphwwa0Wi1/\n+MMf0OvDweett97KunXr+OSTTySgEEKIWOUMhtoNJtrjU8PlOpylEN3qovPH8u5/P+PVV1/lkksu\noaGhgWXLlvHxxx/jdDqxWCycc8453HrrrZjNZlatWsUTTzyBoihMnTqV3NxcFixY0G65W265haSk\n8CCNuro6Fi9ezKZNm6ipqcFisTB+/HhuueUWDIbwfB1VVVUsWbKEbdu2UVlZSXZ2NjabjcmTJwMw\ndepUysrKAFi9ejULFixgypQpTJkypdVj02g0aLU993yTgEIIIcSAVlBQwJ/+9CcqKipYvnw569ev\n5/7772fYsGGUlpby2GOPUVhYyJw5c7joootwOp0sW7aMJUuWkJ2dDcAzzzzTZrkFCxYwZ84cAJ5+\n+ml27tzJ3LlzSU9Pp6SkhMLCQoLBIDNmzCAQCDBz5kz8fj8zZ84kKyuLtWvXMn/+fHQ6HRMnTmTJ\nkiXccMMN5OXlcccdd2A2tx6X1NDQwD/+8Q8OHTrEvffe22PtKAGFEEKIAW3QoPC4n8rKSqZPn851\n113H4MGDAcjIyGDChAm89dZbABgMBhISwmM1kpOTMZlMAMctB/Dtt9+Sm5vL6NGjm49ZuHAhoVB4\nLM7atWvZu3cvCxYsYMyYMUA4I1FUVMTLL7/MxIkTSU5ORlEUDAYDKSktp2Ovr6/npz/9KR6Ph6FD\nhzJ//nxycnK6pc3aIgGFEEKIAa1pGXatVouiKLzxxhts2rSJqqoqgsEggUCAYDCI3+9vHqNwtEjK\nFRQU8Prrr+Pz+cjPzycvL4+srKzmcxQXF6PT6TjzzDNbnDsvL4/169fj8XgwGo3tPo6EhAT+/Oc/\n43Q6WblyJbNmzeKhhx7ie9/7XhRa6fgkoBBCCDGg7du3DwhnKmbNmkVFRQW33347p5xyCgaDgTfe\neKNFpqEt99xzz3HL3XTTTYwYMYKVK1cyd+5cAMaNG8edd95JWloabre7+Q6SIzUNAK2srGTIkCHt\n1kFRFLKzs8nOziYnJ4fKykoWLVrE8uXLO9UuHSUBhRCi3+voSpnQC6tlil6zdu1arFYrVVVV7Nq1\ni1mzZjFp0qTm/T7fsSfT2L17d8TlJk6cyMSJE/F4PGzcuJHFixczb948Fi5ciMlkIi4ujueffx5V\nbT3qt71bVLdu3YrX622ViRg5ciQbN248Zt2jSQIKIUT3SMwm+P2HIT6jV6vRmZUyoRdWyxS94s1/\nf8COHTuYO3cufn94tdLk5OTm/fX19axbtw6g1Zt80++RlPN6vXzyySece+65mEwmjEYjF1xwARUV\nFbzwwgsA5OTk8Pe//52GhgZGjRrVfK7Kykr0en27d2ysWrWK9evX8/rrrzffLQKwY8cO0tJ6biVV\nWRxMCNE9tAYwDQl/70WdWSkTYmS1zLr9aDb8Fur292o1Ot56sUdVVarqglTVBTnkCvD1fi9PvlPJ\nouf/xrRp0ygoKGD48OGYzWZWrFjBvn37KCoqYvbs2YwfPx6AzZs34/V6MZvNqKrKhg0b2L1793HL\nbdmyhUAgwNKlS3n00UcpLi6moqKCbdu28f7775ObG56pMz8/nxEjRvDwww/zxRdfUFZWxsaNG7nr\nrrsoLCxsfixms5nt27ezY8cOqqurueaaa/B4PDz44IM4HA727dvHiy++yOeff85PfvKTHmtjpa20\nSj90FvBFRUVFcyQZS/R6PRkZGcRq/foCacOu669t6Ch3c8Nrjk6VXX6tFWsbsy9+4/EzbU91h8/3\nygkWRhs7MOtlbQnaTXN6ZbbRQEhl4yEX7+yrYsqhvzCYWoyKH5PiJVHjRXtUmOFXtdQH46gPxuEL\nhT9JDzYbMOg69rlVITw4MhgMtrjC8dbyaGqrtjz69iHe21rf4hrmeA052XH85LpfcfZ5hyd++uyz\nz1i0aBFlZWVkZ2czffp0cnJymDFjBqWlpcybN4+cnBzuu+8+vvnmG0aNGsWSJUvYtGkTzz777DHL\nZWZm8txzz7Ft2zbcbjepqamMHTuWG2+8sfn2z5qaGp577jk+/fRTXC4XqampXHzxxVx//fXN2YfV\nq1ezaNGsAXNFAAAgAElEQVQi/H4/s2fP5oILLqCoqIgXXniBoqIiAIYMGcKUKVMinqzrWJpeG4Cz\ngf+1d5wEFDGgv76Q9yRpw67rr23YXQHFbzZsJbnOFfG5akxmHh13RkwHFKqq8k1NA+/sr+K9/U6q\nfH7O0O/nioQtXBa/lcHa2hbHH/IlsrLiTP5dMYYvXcM4evWN9tqvLft8QVyhEDqdDovFQnV1NYFA\nALNGw1BDBJMzHSOgOJZYnRo+lkQaUMgYCiGE6CB9VRUbrrsCbSh0/IMbBTRa1m/8DLJ7fu2H4zng\n9vLu/mre3V9NSZ2XUboKrov/kh9btjBKV9niWFcgjvcPnca/K8bwqXMUIdp+szdoFZLjI3uLqQ6E\nuHJ3JYdbs6L5Jy2w+sR0LB3MdIieJwGFEEJ0kD81lXF/XdHxDEVqajfWqmNqfQE+OFjDO/ur2FxV\nz2BNDZcnfMmPM77kDMOBFseqih5Xwvc4GDeeauNZZJ1g4CZgYpWHB1fv4YFJJzAiteX8CB25S8ai\n0/DWyDRcoRDfBVV+s8/Jo0NTGK5VMGs0kQUTBjOqRo8Sijy7pmr0sgJuFElAIYQQnfBd9tAWv4e0\nGjyDU9HXutG73L1Uq2Pzh0KsK6/l3X3VrC2vJZE6Jhu/Znb6Fs41lKBRDneBq2gImHPxWibgT8lH\n1SaSAbR1z86IVGPEXRvtCXdraDlFq2PcuZkk1rnQBjswKNaYRij/MfAdFeTVH0D79VKCp90Midkt\n9xnMYOy5uyD6OwkohBjgmvuuAyoWl5vqBl/H+q4F3lQztdZhhIwGUFXi9x/CtOsgmmDkXSLdRVVV\nvqp2887+Kt4/4MQXaGCisYjFKV9ygfFb9ErLOvoTR+OzTMCXch6q3tLj9TVqFIYlxlPhrsPf0QU9\njWntBwiJ2TJWoptJQCHEANatfddeJ8q+/6AOvRDiUo5/fB8U0mlxnZSNJ+uINzFFoWFoBr60JJKK\n92Jw1kV2Mk9lm5+uW3w/0nE+XX9X5+Wd/VWs3F9NmdvNecbtzDV9yURjEQmalt0CAeMJ4SDCcgGh\nuKx2zijEsUlAIcQAdmTfdVuj67s0EM7rRLN7BcGMvH4ZUHjTkqi1DiUUFx4noPH6Me08gDcjBW9G\nMsH4OKrzTgpnK3YeOHa2wlOJZv197fb/a79e2mqbqtGHU/xHBBXVvgCrD1Tzzr5qvnbWca6hhP9L\n+JLJyVuxaBpalA8aBjUGERMIxo/sRAsI0ZIEFEIMcE1913q9ngxzAhWeevx+5bjlBiqnovB4RhrO\nUabmbcbSKszb96MJBDGWVeMdlELtyUNRDToahqTjTUsiybG3/ZP6XB0aTAiEj/e58OgtrC2r4d39\n1awvr8GqO8AV8V+ybPCXZB11m2dIl4wv5Xx8lgkEEkeDIn9nET0SUAghRIRWx8VxX0oKZY1TIGu8\nfpIce4mrPPzGrQDGcieGahe1Jw/Fm2khZDTgHHMiT7jqWFjnIqWL8/+EVPgiNJS3HW4+rNxGulrO\nFQlbeCDjK07UV7Q4VtXE40vJx2e5EL85FxQZFyO6hwQUQghxHFWKwgPJybyZcPhOBuPBSsw7DqAJ\ntD1yUOMPklK0B0+5E9cpQwnF6VltNnFhQjyP1dTwQ0/bi5Idy45QKu8ER/Nu0EpIA5fXreEVyxbG\nGFpOza0qOvxJ54bv0Eg+FzRxHb5WJAxahZGpRgxayXQICSiEEOKYVhmN3JecTEVjVmJwMMgd5Yd4\nuvhgROWNh2owOOtwnTQET1Yq5Vot/y81lSvcbh6qrSXtOJNjVagJrAxaeSc4mgOYmRy/jQUJr/N9\nw+42bvMc0xhE5KPqTMc4a3SMTIvnlZ+P7vbrdIlGj5qYDZoOzFAqOkUCCiEEABX+IC/sPsilcQr9\nbwhlxwUT9Mw7IZM1lsMTH11bX8/9tbUcaDj2ctZH0wSCJBd/xx/w88ygdEq1WlYkJLAuLo55NTVc\n3nKoA25Vz5rQifw7OJov1UwuMn7LrKRVXGD8FoPSMiMSSLDitVyIz3Ieqj52Js7qrKg/D01DCI17\nJBpnEschAYUQAoCKQJDCkgrOPTGDFF3/SmEPc5aS5Inw9k3Ac1I6e358VnMwkRUM8qTTyYVeLwBt\n3MQZkbENHn5SXs68pCReSUykUqvlttRU/qW38nBcKrvdZt4Jnsra0AjOjtuDzfQpfzYWkahpGcAE\nA1r8PiOeU2cTSjunk7WJTf35edjfSUAhhOgeMZJq1ldX8d+lN6NVOzbJVGCOlsGlpfwwPp4/1NaS\nFKWFFJNUlSdqarisoYF7U1LYq9Ox0pzG+z9+gQs/WcFPKz/kofh/kKptOdtmKKjB7zPi8xoJBXWA\nQkjf1ryVQvQOCSiEOJK7HAJtTJusS4CEQT1fn74sRlLNfksqE25eetwMheekdGomnEgoITyvhEtj\n5N4aLz/3dax7IxIHG3zs3FFGVuk2Bp8U4sr4L/npgVUM85bCEUMfQiEFv8+I32skGNBz9GqevWV/\njZc6b+vBqKY4LUOSu2cAqIh9ElAI0cTnQrN+NgqtP4mqiobQeU/FxEJC8mLecXtTBre7L2gyUHXF\n6bjPODxDpOnTPVhWFnPOFAPEd22NisMXCvHf/dUsLK/lkLOEyxO+5M/xX3HywfIWh9Vp41k56Hzi\nXQHG79mKEiNBRBNnQ4Br/lpEqI2EjVaBt6efQUqEq4yK/kX+6kI0MZgJ5T/efoYiBoIJeTGPHhVw\nn5lF1RWnE0oMZyW01W7S3viK+B2Vxy4c8UVUqPeCy01GQzkB41Z+n7CF3MH7jqqLDq9fz+vZk5mV\nN4t6fThNcd7B/1G4fiHD68uiU58oSInX8fp1Oe0GtfL8G7jkLy/EkZq6NYI+aKiA+AzQRrYEc0+Q\nF/PoCJriqLzydBpOP5y5MG0owbKyGI2voytSHUVVweuHWjdJddX8MG4rV8RvYVzSLrQtbvNUCJjO\nxJt6IX7NUDRfPMmVtZ8w7FAlM/JnsT3lBD7OOosJP17K7//3Z653/BtNG9mz3iCZMNEWefURoi31\nB9BumkPw3Dkxt0KhvJh3ngrU52ZTfflpzVkJXWU4K2Hc1cWshM8PLg9xrhou1n7NFQlbmJDpIK7V\nbZ6n4LVMwGc5H1XfuA5HbQnacjeKO8DYkk189L9pvHLyZF4/cRJBjZa/6wsozhrErC9fZog7PBOm\nmqCji6GPEFElAYUQfdTuygZ+v7KEeZNHMDItvsvni1MUTkkwEtdP13cImOOomnI6DTmHsxLm9SWk\nrOpkVkJVwReAugbi62o5X/MNP4zfxqT0IkxH3ea5UxnMW7p8JgyfxMikE1qdSlPtJO3eTzgigcHd\nfMLd/L79y2sUygqchJI6XvVYFvXnYd1+NFsXETrjdjANic45RZskoBCij/IFVXZXefAFo5MGP9Go\nZ+3YU6moqMDv79hCVbFMBeryhlD949MIxYdvYdVV1pP2j68w7q7q2LlUle0uD2/vqyLjwB4u1mxj\nYnwR49N3EqcEKCeZZfofMc3/HwJo+JduHG/rvs/XynAMGoVL4ttebjxkSaHyiQIUd6DVPr9Gy+sn\nTuKVkyYT0IZfskdX7+auHa8z1NL/piCL+vMw5EepPwAdXHxNdJwEFEKIfuugRsPvRmZRmZsY3hBS\nw1mJ9xxo/JFlJVRVpbi2gQ/2V7Oj3MEZfMU18UU8ltZ69dD9uqE8pZ/CiYPPIy3xRM5QNJzRuC9F\nqyFL3/7CXMFBbd9NogDX+j9hzMH9zMifxZfpVjZzFv+YcDkzKw9yawBkUmkRCySgEEJ0j15MNauA\nPT6eOcnJ1Go0AOgO1ZP2jy8xllRHVF6Ng799V0qg+Gvy+IpfxhcxIrl1RsNnGEow5fv4k8fhi7PC\nrkoyU0ZzcpRneRztLOGdlXexOOdq5udeh1dr4LFBJ/COz0eh08lpgdbZDSF6Up8JKGw223Dgj8D5\ngA94D7jLbrfXHrOgEKJ39FKq+YBGw+yUFNYYjQAoqopp3W5SVjvQ+NufLbMpiIgze8lP284k0zdc\niINUs/uo4xS8CacSSsnHl/x9Qsahh3cGuvcuDJ0a4o6v7VyydwO/zp/J54NOY6vBwI8yMrijro47\nXS5i554kMdD0mYACeBv4DBgGWIC3gPnAzb1ZKSFEbFCB1xISeDApCVdjVmJUIMCMXaUUvvNNu2XU\nOEhNqeXCtGImJn5DQVx4PMSRAhjwmfMIWfLxJ52Lqu/dsQsn1+5lxXuzWDZpMY9lnIBHo2Gh2cxK\no5EFTidj+tEYGNF39ImAwmazJRMOJn5jt9sbgAabzfYicEfv1kwIEQv2a7Xck5zMR41ZCY2qcnN9\nPXfX1vKd29PiWBVQjSqjUsu5KPUbJiUWkWvY1+qch/yJeJPGkpR1Hn5zHmhi63ZdrRripuqDTCSB\nu1NS2BAXR7Fez2Xp6dxWV8dMlwtjb1dSDCh9IqCw2+01wPSjNg8H9vdCdcRAkJhN8PsPhye2igGl\nLh81DS0/NZdUeVp8P1JyvI7B5v6f/FaBVxMSmJuURF1jVuIkv58FTidnH/EpXQUUY5C8QXu42PIN\nP0j4hhG61uMhdnoy+LBiNGsqc/jSNZS/XDsaa3KUpt7uJiOCQeyVlbyUkMDDSUnUazQsMptZZTRS\n6HRyjmQrRA/pEwHF0Ww22/eAXwGX9XZdRD+lNcTMPeulLh/X/rWo3dtDH1y9p9U2g1bhtety+nVQ\nsU+r5e6UFD6OC2cONKra4pO5P6SyuaKCnXvW8chZn3NRfDEWTUOLcwRVhf/VD2dNRQ7/qRxNSUN6\nLzySrtMAv3S7udjr5d7GTM1OvZ4p6encWF/PfS4X8VFaLbXPiUshNPIKiOt/t9jGmj4XUNhstgLg\nX8C9drv9Px0pq9PF5sNtqles1q8v6M9tWO/3dXiuCV9Qpd4Pen3kNxSWBEJctbGYwuEWRnSgXHtU\nnQ6V8N9EicL5moSAvxqNPJiYSH1jVsIaCPCUy8UZPh+by/dReeAjBns+50LDDi7Rt7yv0h3Ss67m\nZNZUjOajKivVflPbF2qse0faMEEJcUqCkQS9Dr1OE3G5prbqqKPbdiRgd7l4zefjDyYTtRoNfzaZ\n+CA+noUuFwV9IFsR7ech+gw41db18wxgkb6u9qlXX5vNdhnwMnC73W5/paPlLRZL9CsVRbFev76g\nP7Zhma9zWQaLxUJGRnLExx90ufnWfZA4k5kMc8fS/MG6ckLemhbbAhoXtYBZ40KncbbYp4lLRmvq\n+HLwuwn3fa5p/F0LzAwGmbzzc5x73me/eyMX6xvnhzhiyENFwMSa6lNZU57Dp84T8YYie6PqaBtm\nAGuzMiM+volf46RjU2yFWSwW9Gmtu+XuBK4CbgX+DZRotUxJSeE24HGg95e5a19Xnoeid/WZgMJm\ns+UDLwJX2e32DztzjurqagIxeK+2TqfDYrHEbP36gv7chtXV9Z0sV02FwXf8AxvVNk4/XVtbS4Un\n8muqDYdQ197d7u2hteseb71Ro0c5fz5KfGRdDCFgudHIQyYTbkVBGwpwZekmphat4Iz6TQxvGg9x\nRJxQShb1Sd+n3jiWa94IoBJ5xqBJR9uws89Dteb4c2O0pbq6GiVU0eY+A/A88EZcHL8zmajWaFgM\nvB0MUuhycWGMZis6+zw8muouB38b5fWJKAkdD2YHsqbn9XGP6+wFbDZbFpAHZAF2u93ustlsRrvd\n3nqEWBfZbDYtsAyY3dlgAiAQCMT0lMKxXr9o2F/jbXelzGgsetUf27CzAVJH2yLY2K0SDAbxH2O+\nhlbc1Wg7OtdEyE/AXQ2643/6L2kcK7FVG+CHpR9wael/mXzwv6SqdeEDGl/FgqpCiXIS3uSxDMq6\nAEP8UAxAebkbFUfH6teos8+nDpfTxKPR6FE60I6qRk9QEw/Huc4Vfj/5bje/S07mnfh49mm1XJOS\nwrX19dxfW0tyjI2t6PTz8Eg+F5q1M1Ha6EhSFQ2h854CQyznafqmDgcUNpvNADwLXE94LJAK/Mdm\ns6UBH9lstvPsdvt3Ua0ljANOBZ622WzPNF5TafxutdvtrefAFTHH2RDgmr8WEWrj9UurwNvTz5Dl\nt0WzEPCqpp5d7s3cu+cjLqzcSNxRb7gNqp4dmtPBMo7BWedhMfTRgXfGNEL5j4HP1XJ7/QG0Xy8l\neNrNkJjdcp/BDMa21wY5WkYoxNLqav7d0MDvkpM5pNXyWmIi/zUaeczpZKLXG6UHEiMMZkL5j0PA\n3XqfLkGCiW7SmVfvB4ErgVmEuzI3NG4vB4qAR4CfR6V2jex2+zrC3aWiD0uJ1/H6dTntZigkmBCo\nKt66Xeys+ARTYDN31he3OuRQyMRObR6G9AKGZo1liLafzLZgTGs/QEjMhqQRXb7EZR4P+T4fDyQl\n8WZCAqVaLdenpfETt5sHa2pIjbFsRZdIt0aP68wr+DTgVrvd/g8Am82mAtjtdrfNZpsDvBO96on+\nJhrdGj3C60TZ9x/UoRfK7WbdTQ3ic35FVdla0t2fkaVUknXUITvULPZpc0kcNJ6RmWM4WSOfLzor\nNRTiGaeTyxsa+E1KCqVaLW8mJLA2Lo5Hamq41BP1XmsxQHQmoEgHvmhnXzmxPYBYiMh4nWh2ryCY\nkdcvA4qD/iDOYMv+6e8a+653ef0EAi0XtjreSpkdF8Jf8zl1+14m07OZVMXNYAh3ZAJBNKy35PK1\n7izOMIzBmnEiqUrnFttKjtdh0CodvvXWoFVI7sdZs0leL2PLy5mblMRriYkc0mq5OTWVSxsaeLim\nhoxQJ8cvdEDvPw9FNHXmv2UXcCHhO7iOVgDIeAYhYthBf5ApuyvxtfP++pt9zlbbDAr8c2Ral17M\nFSWI3xCiQQ+ZBifJh54nE5qDiDptPB9k5PPvzAk4zOfyiEfBFoxsifFjGWw28Np1OW3ONPrg6j08\nMOkERqS27jYZCLONJqsqhTU1/Njj4Z7kZPbrdLwTH896g4GHamu5sqGB6K6ZelhvPQ9F9+lMQPEm\n8IzNZhsKvE/45eB0m812KTCH8IqgQogY5QyG2n0Rb49PDZfr2Au5ikYbwK8PETSEyNC3Xhi4LJTE\ne5nn888Tfsh/0scS0hj4tcvFw3V1RG8qrHBQ0V5wMCLViHVQdOY72Onxc9XGYp7ITmJ4H3rPu8Dr\n5cOKCh5JSuKviYlUa7X8ymJhRXw8jzqdZHVDtqLnnoeip3QmoHgIyAbuBx4gHFC8BQQJ3/b8cNRq\nJ/qtQ/V+3tp6iCvPSCc9MZpvHaJ3qWh1fgKGIBq9H7OuodUR3/oHsck7iv9Zf8zzZ/wQnzb8MnSm\nz8fCygpO7cPziHhVlW/dHryqGbrts333MKsqj9bUcHlDA3enpLBHp+N9o5GNgwYxp6YGWzdmK0T/\n0OGAwm63+4HpNpvtD8A5hMdMVAOf2+328ijXT/RTlfV+/rKplPNGJUtA0deFPOgMHoL6IAaDB6Om\nZUAQVBU+943gf57hKD4dGcmw+KKb+F/GaAAMqsosl4tb6+r6zkx7PUWjR03MBk3P/Y/k+3x8UFHB\n42YzzycmUqvRMLMxW/FkTQ1DotANJfqnTv//2u32g4TX1BBCDDCK7xCGmk8JVa8nof4rtOaWbzL1\nIQNrvSez1TOMBH+IAuU7bNpvWXzG1dyXex1ebbj7Ic/nY4HTySl9OCvRrUxDCI17pMcvm6CqPFhb\ny2UNDcxKSWGnXs9HRiMXGgz8vraWn7vdnZh3VPR3nZnYav3xjrHb7fmdq44QIiapKgme3Ridn0P1\nBhI8O1odUh4084FnNMWeLNICPiZodpGv2QxaKE4+gcsKHmRL+qkAxAV93F15gJsDBslKxLBz/H5W\nV1SwwGxmsclEvUbDb1JS+Fd8PPOdTkZItkIcoTP/yz5oNZ+pifBMlqXAZ12tVKzq7mmjRQzphVRz\nrNGpAc4Jfcuk4P/4QWAzw3YfanWMw5/J+57R7PJkMjxUyw80O7lUs6f5lSWgaFh0mo3CMT/H15iV\nOLuiiD9+UsioU2+MymRNonsZgd+6XPzI42FWSgrFej0b4uL4QUYG97lc3FBfL7MOCqBzYygmtLXd\nZrOlAC8Bb3exTjFJpo0eYHop1dzbTGoDFwS/4gfBzVwU/JJkWk5dHFQVPvON4P2GHL4znMUZWSdx\nkcnFL756iKNz4N+kjOCu/Lv5Kv0UAIwBL7O3vMDN3/wTrRqitz/bGrQKI1ONGLQy1DASuX4/Kysq\neNps5hmTiQaNhgeSk3k7Pp5Cp5OTpNtqwIvaO6Ddbnc2DtR8HfhbtM4bK2TaaNFvqSoXhLbyS/8H\njA9tw0Dr8RAfeU/hw4bRlMefxdjBQ7gqK5nB8Y23Yda2DDr8ipZnTr+WhWf+DL82nOE5p/xrFq4v\n5KTafT3ykCIxMi2eV34+urer0acYgLtdLiY3NDAzJYVtBgOfGwxMyshglsvFLTKwdkCL9t/eDwyL\n8jljhnRriP4kTvVxRXAD032rOIUDLfaVBc186BnN6obRbNCMxmtK4k/fyyQ/+djzNXxtGcWM/Fls\nTTsZgPiAh99sXs6NxSvQqt0/82Jvy9BpmTUikwydQnh5s/7ptECAfx86xGKTiYVmM15F4ZGkJN4x\nGil0Ohkt2YoBqTODMie1sVkBMoDbaXsGTdET3OXtr64XYwvlSLq59+gCNdzpeZNfBNeQoRxe3bIi\naMLu/h6rG3L4SjcS1ZQIFiPowj3klrj2x5P4NDqePv1a/njmzwhowi8rY8u2snB9IaNcB9ot199k\n6LXck51BRUVF55fe7iP0wJ11dVzi8TAzJYXNBgNfGgxMzsjgLpeLX0V5cjIR+zqToVjF4eXDj1ZN\nePEw0dN8LjTrZ6O0Gi8LqqIhdN5TMbVkr6SbO2aYs5QkT13Ex9caTYC1xbay6p349v+DXN8njFX8\nzf/Bxf5Mnq8bz7/Uc/CakyDtcBARiW1xiSzIuYfyUDJnbPkKY8DLjcUr+HHJR2jaeD4CqAm6Xh9D\nIaLjlECAFYcO8efERJ5ISsKjKMxPSuLd+HgWOJ2c4fcf/ySiX+hMQHFhG9tUwAnssNvtbXxEFt3O\nYCaU/3g4Q1F/AO3XSwmednN42WNdQkwFE6Jj9NVV/HfpzR3qMggoGj69ZgP7EzUU793AcNe/Gasr\nCu9sDCTWek5mme9CPjaOgcz4DgUREL7d6ymzmb/pdew/6Sy0HZieWdUolBU4CSV16JIDT91+NFsX\nETrjdjAN6e3atEsL3FJfz0SPh7tTUtgYF0eRXs+l6encXlfHDJcL6TDu/zpzl8dH3VEREQVHd2sk\nZsttef2A35LKhJuXRp6h0IIrzciFFf/hx87/8FPDweb/dK+q5b+B71HIRL5NGtXhIKLJV3o9M1NS\n+EavB7OZM4u2MuvTF7h8z8ftZiWOpCboCFn63yquURfyo9QfgFDf+JQ/KhjkH5WVvJiQwCNJSbg1\nGp42m1lpNLLA6eQsyVb0axEFFDabrSP3z6l2u/13nayPEB2yzxfEFQqhC6hYXG6qG3wEAgHMGg1D\nDf3n7vi9KYOPuV/VQjAREpMa+FnqJn5p2sBg7eHFuGrVRBzGi0kZ9hNM+lS+3VPdqXp4gYVmM8+a\nTAQblxMfV1/DH79+jBOUUkIjzP14KKKIhAa4we3mYq+Xe1JSWBcXx3a9nivS07mpvp57XC7i1Q6u\nCib6hEgzFPd14Jwq0K8DCm8gxP4aL0OS44jTyQS0vaU6EOLK3ZVHvIFVNP+kBVafmI6ls3+fPpBq\nbgoigiYYlljF/zN9gi3hcxI0hz8FVmsG48m4krjBP+RETXiJ7nKPn+EH9pFc52rv1K3UmMwUn5LJ\n7RnpOPThoXYJoRC/r63lF6VF6OtKo/vgRJ83PBjktcpK/paQwNykJFwaDc+ZTLzXmK1I6uTzkBMs\n3Vhr0RURBRR2u13eNY9QUuXhhtccLL/WGrVlj0XHWXQa3hqZhisUwoWGdT6V8QYFMyHMGk3ngwmI\n2VRzcxCRCKpR5ey477jJtI5JxiI0yuFPfRtrRpI88hoGDT+POKVlO+irqthw3RUdGvMQ0GrJPniQ\nisZgosDrpdDpZFgfnnp5d2UDv19ZwrzJIxiZFt+lcw2UTFlHKcDP3G4u8Hi4LyWFNUYjJTodP0lP\n5+e1ro4/DzVa1m/8DLIzu6/SotOiOg+FzWbLBF6x2+0/iOZ5RQclZhP8/sMQn9HbNel24RdrLdsD\nKi/vqeDyEzM4uZPjAmJVrT9AwAwhE4SMoFVC/Cj+a24yfUyu4fBEUf6QhpWHzuTF/QV8XTeE5TlW\nBimtgyp/airj/roiok+GAZORuhGDqczKpCIjg8RQiPtra5nmdvf5pax9QZXdVR58wa6l37s1U9ZP\nDAmF+GtVFW/Ex/NAcjJOjYaXR41kw9ZtDPnyWwy1kY3lrzGZeTQ1tZtrKzqrUwGFzWYbA/wASDti\nswKcBXw/CvUSXaE1xGyaXkTG6Quw5mAN7x+s5rNDdYQyFEyKh2sSPucG0ycM1Tmbj60NGHn94Lm8\nfGAcZb7kiM7/XfbQY+5XNQp1IwfjHjYIGsdKnO1u4FlXLUP7cFaiOxyZKdPpdFgsFqqrq5szFAM9\nmGiiAFc3NHCe18tvk5NZFR/PzpzR7MwZTfyBQ5h2HEATlBE4fVlnJraaAtgJB99Hz0fxHfCH6FRN\n9GfRTDf3F7W+AGtKa3j/oJNNh1w0fXDO1jq5PnE91yZuIknjbT5+b4OFvx4o4M2ys6kPRu+mPF9S\nArWnDieYGB5zoQSCmHbs5zGDylCjTFXUlqZMmV6vJ8OcQIWnHr+/r+dwukdmKMSfq6tZXFvHIxYL\nqkFHQ3Y63tQkkhx7iauKfEyFiC2dyVD8HngCeIhwbm8M4AZuAM4BlkatdqLfila6ua9z+QP8t7SW\n9w86+bTCReCI0e9n6vdyV8oGJui/RKsc/uS2uXY4y/eN54PKHEJHr8jVBeGsRBbuYRnNWQlDZS1J\njgiCxfUAACAASURBVL1ovX4UGQzXc5pmvfW7CGVfAH4X1JbE5Ky3naEAF9a7Wbq9lNqTh+LNtBAy\nGnCOORHjwSrMO/ajCUgmrK/pTEBxCnC13W732Gz/v737DpOsLPM+/j2VO6fpyXnIQZLOECQIgllA\n3QdWWFZRWVfWRQR2FVHBBOqyJlj3ZUWRzGMAzAwSRCWqoMiQJyemp6dzV67z/nGqp6vTTHdVdVdV\n9+9zXXP19DlVp+46VV3nrifcj3EBx1q7HbjaGPNJ4Dq85EJERtGbTPO717yWiMfaekjmLGHrI8PZ\nja/w0bo/sjjz0u7tadfh/p0Hc9OWN/JMz+KixuMC8dmN9C6bR7raa+lwkmnqXtlCZPuuih8rUXFG\nq3q71Sv/U45VbwvhS6ZpXLOB2I5OevZbSCYcJDavGXBpeGFTqcOTCconoXAY7OboAeYAr2Z//ylw\neRHiEpk6sXZIDGtm7ds69GeuUB1EWkZu34P+VJpHXuvm/q2d/LGtm0RmaMvMPtUun5z9HG/iQaqS\n23avK+X6qthV92bOffAA1vWPv4Ug5Hdo2MsKuLsTiaVzd3dvAITau6h/cTP+eHnNcJkxcqveDjdN\nq95GdnYR6uylZ58FxOY0EeiP7/1OUnbySSieBv7TGHMx8DzwceDR7L6VjL7Gh0h5irXje/RTOGNM\nD/U/N7IHz/UFyRx7zV6Timgqze93eEnEH3Z0Ex+WRMyvCvGeeS5nVT3Kgp7V+JKDlTDTwVnEW08n\n3vJWnEAt17Ym6IoOXcFx/a4YV63ewOdPW8LS5siQfQ1VAebWhUaNKw1EZzfSNyyR8Efj1KzbTuS1\nDv0Rl9o06NaYKF8qTcMLG6l/cSPOzO4JrVj5JBRfBu4FrgW+C/zIGHMcsAs4GLiteOGVp6XNEW49\n5wAtZ15Gwo7DftURws4EL4WJnjGTibE4maTXojFKQhFNZ3h0Rzert3by+x3dxIaNWp8bCXLq/EbO\nbOnioOivCHf+Dic+mCikqvYhNvs9JJqOB2fwz3NuXWjMBGFpc2Rc9VDSwM+rqvjqrFa6Q4ODK/39\ncWo2vEbktV35fZCH6nB9wQmdR9cXLPk37ZaaIOevnEtLjQaalhMlE5Urn7U8fmOMORDYYq19yRjz\nXrwVRkPAXcC3ihxj2QkHfCwvwsyESSuGE+/E2fwQ7sI3QXhmrJewIhLkkVUHZJeNntqm+ng6w6Nt\nXkvE717rJjosiZgdCXLqvEZOnVfPEf7nqd5xK8Gtz+ze7+KQbFhFrPVMUrWH7h4QWSxp4GdVVXyz\ntpZXgsMTiWyLRCEf4pEWr8VmlG6jIYvU5cqj26jYZtUE+fDR80oag8h0ks+00cOstX8d+N1aezdw\nd1GjmgEmtRhOvBPfuntJtx5RFgnF9p7Rm+tzf+baU3N9uUhkXB57rSubRHTRlxqaRMwKB7wkYn4j\nr2sIUtXxEJHXfoo/NjjQzHXCxFveTKz1DDKRPdeFyMdAIvGN2lpezUkkFiST9Ly8lciOAhOJXJGW\nsRMELVInMiPkNYbCGPN34GbgdmvtKKPWZG9mSjGc7T0Jzr55zZjTQ69avWHEtpDf4c7zDiq7pCLp\n+ng8s5jV6f148LEuetNdQ/a3hAOcMtdLIg5vriGQ6iK8814ia36OLzV420ygiVjru4jPejtuYHyF\nqCYiDdybbZHITSSWplJ8oqeHAzu6Oe+1/BYHExEZSz4JxfsBA3wBb6row3jJxU+ttX1FjG3amwnF\ncLqiqQnXmkikXbqiqbJIKJKujyczi7gvsx8PplfQw9DBj40hP2+e18ip8xo5sqUWv+Pgi20ksulG\nwrsewHEHu19SkaXZ8REngq/4zy1FNpGoq2NtYPBPe1k2kTgjGiWAN5JaRKTY8hlDcSdwpzGmDjgD\nL7n4HvBdY8w9wC3W2vuKG+b09mosyXufeIGvza9n8fRahqIipVyHP2UWsjqzHw+k96GToeNlGohy\n8twGTluyiKNaagn4HHBdAr3PENlxN6Hup4bcPlF3FLHZ7yFVd0TRx0eAl0j8ZByJhIjIZMr7c8Za\n2wPcAtxijGkETgf+HfgV3jAAGae46/JSf4y4W4dm3ZZG2nX4c2YBqzP78dv0PnQwdNZEHTFO8b/C\nab6XWenbhG+/z0F9HWSShNp/R6TtbgLRtbtv7zoBEk0nE5t9JumqpZMSs9/v0NJSxQWzZ7G+aXCs\nzPJsInG6EgkRmUIFf94YY1YC/4DXWrEC+EuhxxSZCmnX5ZnOFL9NnsRv0/vSTs2Q/XXEeZPvVU7z\nv8TRvo0Ec8pfZ9J9hLffRaTtZ/hSuwa3++uJt76D2Kx34QYnp1R1Cri7qopvHTibdYeu2L19eSrF\nxdlEQhm9iEy1fFcbPRoviXgvsBhYC9yOt3T5i8ULrzzt7Etyz7M7OePQWczSHPaKknFd/tbRx+qt\nnfx2Wyc74yng8N37a4hzkm8tp/lf4ljfRkLO0PUEfL4Uoap+Qhsux3ETu7enwwuJzT6TePMp4Juc\n+iQDicQ36+pYn9O1UbaJhC+IWzMffOX5NxJPZdjSFWdBQ5jwNBkELVJK+Uwb3QTMx5vnaPGSiCeK\nHVg5a+9L8v0nt3P88obyTCjK/IN8MuxpHIrrujzb2b87idgRG1qnoopENol4meN86wk7wxclcvEH\nkoQj/QRCcW8YRHacabL2MGKzzyRZ/wZwJueilAJ+WlXFt4YlEiuSSS7u7eXd5ZZIDKhdQOaYr5Q6\nijGt3xXjg3e+yA/O3n9chcFEZM/yaaF4CK8a5m+ttVoOrhyV+Qf5ZBg+DsV1XdZ0RVm9tYP7t3Wy\nPTo0iYj4fRw/u57TGhMcv/Y6qpzUKEd1CYbihKr6CAQG97suJOpWElvwT6Sr95m05zRWIrFPMskn\nyjmREJEZKZ9ZHudNRiAixbC2J8qvtndy/9ZOtkYTQ/aFfQ5vnF3PqfMbOX52PVUBP3Svx79uWDLh\nZAiFo4Qj/fj8g+Mm3IxDPF5FIlZNap8PQvXSSXkOA7M2vj1KInFxby/vUiIhImVIg8Cl4rXFkty1\n0SvU9Ok/rYOcVTJDPofjWr0k4oQ59VQHxr4UO7404Ug/oXAUxzdYOyOT9hOPVZOIRYDJ62tP4rVI\nDE8k9k0mubinh3fGYkokRKRsKaEoA60BP5csnUNrwAEye729eF7o6ue2tW3ct7WTVCgAi1sBCDgO\nx86u49R5jZw4p4Ha4J4vw/5AklCkj+DA+IisVDJIPFZNKhFmMqfzDiQS36qrY4MSCRGpUEooykBr\n0M9l81uzC1spodiTjOvy+x3d3La2jT+1947Y/7ED5nPW3Drqgnt5a7tpgl2PE9l2B8GGwWmfrgvJ\nRJhErJp0anIrdSbxuja+VVfHRiUSIlLhKiahMMa8Bfgh8KC19v2ljkfGb1HndupjIy/+Y+mO1AL7\nD9kWTaX5+eYObl/Xxsa++O7tEZ/DuxY1c+ziVi7e3s2J8xqpC+yhNSEdI7xrNZEd9+BPbNu92XUd\nErEq4rFq3MzkXsbHSiT2Syb5xAQSiXXtUa749Xq+9LalLCvC6rciIoXItw5FM3ABcCQwDzgb2A68\nzVr7i+KFt/vxLgPOB14q9rHzEfI7LGuOEPKrquXeBDt28fANF+B3x9/yknJ8PH7WYzC7mh3RBHet\n38lPNrbTnRycVNQYCnDaomZOXtBEXSjAxux6IWvjSVKpoa9Lo9/HfDqJtP2M8M5f4UsPJjdpfxOJ\n7iSJeBW4k1uLIAn8uLqab9fWjkgkBlokJhJBIu2ybldswmuliIhMhnzqUBwA/A6oB54DXgeEgX2A\nu40xJrukeTFFgZXAt7OPVVLLWqq47dwDSx1GRUg2NXPSBTdMuIXiA6Fqbnx6A/dv7SCVc71cURdh\nfU2EztoqrONgt/UMue+nN3cO+f2gzAYuSP2GA9NP4nMHZ3Okqvb1FuryL8L/1Bfze3LjlAR+lE0k\nNuUkEvtnE4l3TDCRqBi9W/A9ez2ZQy+E2gWljmaEpc0Rbj3nABY0lPwjRWRayKeF4lrgWeD91tod\nxpgeAGvti8aYK4BPAUVNKKy11wEYY4p52OmrzD7INzXOHdftXCBTDakG+MxzG3dvd4AT5tRzzrJW\nqmvCnLuxc8xjADhuhhMzz/KR5G84LrMm5/gOyYZV3kJdNYd4C3V1r8/jGY1PgsEWiRmVSAzIJHH6\ntkImuffblkA44GO5uopEiiafhOKNwJustTtG2fcT4POFhTR5AoHyHDIyEFex4nN9Lm7fVgI+FydY\n2mqZ43lOrgPpOkg3gBsc7K6o8vs4fckszl0xhyW13rLha4bVlsgVdhOcmX6UDyXvY1936+7tUUJ0\nN59KzYJ/wK1aiAMMnBU3ECCfDoNAIDDmuU0Ad0UifLO6mk3+wdEQB6ZSXNrXxzsSCS+RKPC1yX3f\nBCdwrEAqvy6SiT7OwLnd07mabor9tzydTdX7UAo33vdzPu/6FNA/xr4qynjeY1PT5CzWVCzFii/p\n62RX9njBltaiHDNfryXGninh+r3WiHQdkDseJeXyoYOXcumqfWmKDL1/U08/XtX3gYO4HOKu562p\nP3N26mFmMdgFsoMGbgqeyu2BN2EPOZIldSPLKw+cq4ka7dwm8EYNfxnYkLP9UOBzwHsCAXwNDXk8\n2ugGzm1TUxOtreM/7opYgvC6ncQz4/9AD/scVsxupTUy/pkv5fQ+nGrl/llTDkb8LY/3fk1NtI7y\ntyyll09C8SxwBXBuzraBT6aPUsarjXZ0dJBKjVZiubRSPh9dkWoaYv0EMoXnY26XV+Spo6MDJzPx\nP9hi6ujoG7EtE4JUI2RqILfwgxN3CXSBrxfeekINqZ4u2oYOkaAjmiDkJjk68wKnpv/Cm9NPM8/t\nGHKb552F3Bh8Kz/zH03CCWbj6KAtNjKWgXM18ec1eG4TwJ3ZFonNe2iRaM/rkfYUQ9/uWNpCY7fc\nDBcGfr7PbDrSQ99rG5IZ/mNjO19b3MKS4NDOmCa/j/Aor8eelNP7cKoEAgGamprK9rOmnHTsobVx\nj/cb429ZJs/A+3qvt8vj2NcAvzDGrAIexGs9vsoYcxDeAM235nHMKZFKpUgmy68/9+WUy1l/28Rd\nK1rZd09THscrlcKP93wp8fMd+FDNHR/hVuU8R9fF1w+BLnBig+Wjhr9WTqqHYPdTrNj1KH+O/pk6\nYkMeJ4mf3/sO4fvB0/iD7+Ahicrg8UY5t9lzlc/zSiST3FVdzXdqa9mS0yR4YDLJJ3t6eGt2jEQ6\n+6/YBs5tPu/rWQ7MGvFe85KIJUHfKO9Dd+J/O2X0Ppxq5fpZU07yTbjG/FuWkstnLY/fGGNOBj4N\nvA/w4yURjwMXWWv/WNwQpZJF0xlS9SPHR5Bx8feAvwt8Y3yu+OLbCXY9TqjrcQK9z+KQoTZnfzdV\nPOx/Hff7j+R3/kPpdmom9bkMSPgC3N44h+/Mmr3HRGKytdQEOX/lXFrKccVbkb1o9PsIOZCYwFCK\nkOPdT8pTXiOHrLWPAI8UOZYxGWOieF9yg9nfzwRca6060srUtmiCO9e18ZMN7aRmDR0fEegCfw84\nw3p3HDIcXLuVk1ue53Wv/S81WzYyXDwwizs4nPv9R/Kkb3+STuGD3/w7+nH69/5tKeELsHrR0fy/\nww2PzV2xe/tB2UTiLVOUSGzpitMb99o8jl/eQHtfkva+JLVhv6ZASsWYF/Rz97IWOod1vW1Mu3x6\ncydXL2xksX9kTZl5eymlL6WTTx2K7cCR1tqte71xkVhry2puV1lVKIy1Q2JYx3bf1qE/c4XqINIy\naeH8vaOPW9e18cC2TnLrLTmx7PiIvqGrYgSdFKsa13JKyxpObn6BOeFub0fO9T1VtYJEwzEkG47m\n784irtzLtNGJ8HV00vIff8QZ57ekf+aPnOP/JnO3b2dOQwOX9PRw2hRO/+yMpjjr5jWMNp7S78DP\nP3wojVVlMsMg3Ehm2ekQbix1JKPa2Zfknmd3csahs5ilVp6SmBf0j0gQBmZ/LA8Hi9MFLFMmn0+e\nHXgVMqcsoSg3ZVOhMNaO79FP4Ywxz9//3A0jtrm+IJljrylqUpF2XR7e3sVta9t4JmcQpgO8vqmW\nv/69Byc+mEg0BPo5oelFTml5nuObXqImMHRwViLjp7/qEIKtx5FsOJpMKGeGQKy4/dKZpkbav3bc\nqC0UCV+A+xYdwx37vJW2qsEBSXPo5GuxnZw2MP1zCjVWBbjrvIN2t1Dkqg37yyeZAAg34q44s9RR\njKm9L8n3n9zO8csblFCIFEE+nz5fAL5gjDke+DMw4uuitXZ1oYHJOCR6xkwmxuJkkl6LRhESir5U\nmns37uKO9W1s6R9MCqr9Pk5f1MzZy1oJZhzOfmYNsyPtnNK8hpNbnueohg3sStZw17aVHNmwgRoS\ndKciPLJrPx5oP4gnuvfne+ccydy6yV2ca0B69tCes7gvyO37vpXvHHI2W2sGk5lDdr3CpX+9hbds\neozMyiuhvn5K4htO3RoiUo7ySShs9ufhw7a7eF9CXdAiidPZ1v4Ed61v4+6N7fSmBvs/50aCnL2s\nlTMXN1MX8OHvf5lQz2M8edLjRBIbhhyjLVHH9RtP4XULmmlrOYie8EG0LAlggI9UBaYsmcg1nkRi\nOjfAhh2H/aojhJ3p/CxFZLLkk1C8qehRSEV4tqOPW9e28eD2oeMjDmms5pzlrZw8u5rqvr8S3HYn\noa4n8KVGloxKVe1LomEVvcmV8HQvoSVnM292NfOm8HkMF8tJJLblJBKHtr/MpX+9hdM2Pz6tE4kB\nKyJBHll1AG1tbZryKCITls+00d9NRiAz2fJwkIdX7k9Nbw+ky6sYTirjjY+4dd0O/tYxWCDVB7xp\nbgMfWFLF4b6/E+6yBJ/7M05maH0I1wmQrD2MZMPRJBpW4WbHQ2R29AMvTuEzGSkG3Nk4l+vOvGlI\nIvG6bCJx6gxJJGaagVky63d579WBn4BmyogUIJ9ZHl/Zy01ca+1n8oxnRor4HBbVVNHW30tyMiog\n5aE3meaeTe3csa6NbdHBb6vVfh/nL0pzTuPLzIo+RWDzGpxh1dYz/lqS9W/wkoj6o8A/NfUhxisG\n3FFdzXV1dWzPqWypRGL6G22WzFWrB7vjym6mzAykrrfKlc9fzaf2sK8Lb6nxaZNQbO9J0BUd2mow\n2jebAQ0l6v8vlq39ce5Yt5N7NrXTlx0f4ZDh5NrtfHT2Og7jb4TiG0eU4E+H5mRbIY4mVXsIFKE+\nRLHFgNtrari+tnZoIrHzJS+R2PKEEolpbk+zZKAMZ8rMQOp6q1z5dHmMmClnjKkBjsFbafTfixBX\nWdjek+Dsm9eMOT0095vNgJDf4c7zDqq4pOKvHX3ctnYHD27rIgOESXJy5BVM/cucGH6eqkwnxIfe\nJ1W97+76EOnI0hHlrsuF63O4u76OnzQ1DEkkDkskuPi1V3nLQxcpkZgM6QRE26CqFfzl8/egLg2R\nyVGUVNxa2wf81hjjAtcBxxXjuKXWFU1NuNZEIu3SFU1VREKRyrg8uL2T29a28WxnP02+Ps6sfoHT\nIs9zUuRlwk52Kmi2R8MbD3E4ycajSdSvwg3NKl3w4+D6HKLzWuhbMofrw4N1Bg5LJPhkTw+nxOM4\nfR1KJiZL31b8T15JeuWVUL+01NGIyCQrdtveOkZOJ5Uy05NyuefVHdyxvo1IYhtvrnqeK2at4fWh\nDfiHlYz0xkOsJNFwNMn6o8BfnGrnIb/DsuYIIX/xL+euzyE6v4W+xXPI5CQSh2cTiZPjcSURIiJF\nls+gzNG+ejtAK3AZMHKuoJSFzZl67kgfzqt/+RsnhJ7nlrrn2Te4Y8Tt0qG52fEQx5CqPRic4pcV\nWdZSxW3nHljUY46VSAS6+7iqr5d/dtNKJEREJkk+LRQxvOJVo3GAy/MPR4rNdeFv7hz+6p/N7OoO\nLor8mlZ/74jbpar3J9GwimTDMaQjS8p2PATA4q2baegdXL/EdSDe2kh0XguZnh3wnJckBXqjVG3d\nSTTtZ9Uxh+JEVF55T16NJXnvEy/wtfn1LFZpOhGZoHxLbw9PKFy8EtxPWmsfLziqGaYtmeamddt4\nR9ihWMsopXB5JdhAJpjmDeHnOcH3tyH7MwRJ1R8+WB8iOHkLhhVTcNcuHjvvdPyZzN5vnJXy+Xn0\niadg/pxJjKzyxV2Xl/pjxN06KKQtp38HpPpHLlIXqIbq2QXHKSLlKZ+E4vvAZmvtiE90Y0yDMWaV\ntfaJwkObOdpSaa5d38bKFa00FrC6ns+XIh1K0x90mBvsYJUztDujPxMimQjjW3I2yTlvA39ZLeI6\nLsnmZo65+V7qYn30L2wl3jqYgg20SAS7+odcDrtq67i6uXnqg52JEj34Hv1PnJzvHAOL1LmOj8zx\n3/JWvBWRaSefhGIdMJcRlQgAWAz8BmgaZZ8Um5vBH0gQDMUhmCQc8OZs57ZybE41sjPewMJkP8G0\nAzgka4+syGQCvAknLxx1GL0r5uEGvbevk0xR++o2qrracebUwEQaIkJ1uL7ghBZZc31BXRTHEqoj\nc+xXvRaK4QLVOm+yV+p6q1zjTiiMMZ/L/tcBLjXG9A27iYNXi2KqV3SeWTIxgj3PEOp8jGDXY/ga\nekbc5OnEIv4eW8CcZIzXu9tY5sSYDi/L3wMBLprfQk9ksI5AZFs7da9uxZdvidFIi7ece2LYeezb\niv+5G0gffAHUzB+6L1RX1OXfpx11a0gBitb1JlNuIi0UK/ASBhdvNsdoosAXCw1KhnKSnQS7nyDU\n9TjB7qdx3MEKU872OIkeh2eSC3kyvox0MsiJzjre4/vrqMdyqwOUSXXvcetxHL5eV8cPamrIZAeL\nBnqj1L20mVDX8Lw2D5GWsROEmvmqoSAiMg7jTiistf8MYIzJAEuAEfMNrbXx4dskP77YJkJdjxHs\neoJA3/ND+qQBdqWr+eOOpbz3grsIuy7H8wLHj+O4rs/hteM6ydRPTtzF5AI/i0S4qqGB17IVLiOZ\nDIG126je3IYzsZpjIiIyiYpSenuAMaYZ+Lq19kMFRVVGFnVupz42cprlWLojtcD+eT2Wv+8Vqnp/\nR6jrcfzxLSP2b0i1cF/0IO6PHcifE0uYHwnQc3Utb4k9T8QZ3yqlbnWATFOx5pLkb117lCt+vZ4v\nvW0py1pGjud41e/nM42N/D482L3x9miUc3e0c/GmnVMZqoiIjENelTKNMXPxymvnthM7wCrgbGBa\nJBTBjl08fMMF+N0JTFF0fDx+1mMwe/wVJYNtvwGOIrLuWqrcwfVBXBxecZfy0+79WB07iFdTrYDD\nEc01fH15KydUdRB6agNQTXkter53ibTLul2xEaXNo8B36ur4bm0tiWz3xpJUii92dXFKPM7z6Urr\nsKkcrQE/lyydQ2vAAcb/nhcRgfwqZZ4A/AKoxWuVHhg14wJpvLU8poVkUzMnXXDDhFsovtA0gSmK\nmSR1r/2YfcNzCZPEdUJsCR7Cj7v35baOFbRlvFHxAQfeNr+Jc5a3clBjNlnp7pzI0yl7D4TDXNHQ\nwMaA97YMuS4X9vZyYU8PlTknpbK0Bv1cNr81u8qjEgoRmZh8Wii+DPwI+DrwJ+DtQBL4QHb/WAM2\nK9KmxrmT+wC+IIuWnM2vU0/zq13/wIc2zWZjbLBXqS7o572LWzhr6SzmVJX/gmP52OLz8fmGBn5d\nNZg2HB+P8+XOTlaoRUJEpCLkk1AcCpxvrX05u7roZmvtWuAxY8w1wDVMs6Risn234whueGku0dTg\nxXNRdYj3L2/lXQubqQ5M38nYP6qq4oezZ9Pv85KoOek0V3Z18a5YrLQTxnxB3Jr54FO5bpGppK63\nypVPQhEGBmZz9AHNwNrs798D/oASinFLZly+tWbz7t+Paq7h3OWzeeOcevxlvJ5GoZ4NehfqG2tr\nSfh8+FyX8/v6uLSnhzq3DKZv1C4gc8xXSh2FyIyjrrfKlU9C8RxwvjHmi8ArwHl4XR/gTSeNFCm2\nGSHoc7jisCVsTzuc2lrNvjXTs1tjwE6fjy/V13Ovm2RedtuRiQRXd3ZySKrShpaKiMiAfBKKbwA3\nA3cBNwE3GGOOwivFfTLwYNGimyHOWj6b1taBjHz8JaArSQa4rbqaa+rr6fT5CHV0AXBRTw//7vRN\ngzqeIiIz24Q/x621twEnAeuttd8DLsHrBlkO3AF8pJgBSuV7Nhjk3bNm8anGRjqzYyXe6U/zj6vm\nca4/rWRCRGQayKsOhbX29zn//wZeq4XIEN3Zktk35ZTMPjCZ5OquLt6QScCqSZ5BIxMSy7i80Bel\nJuMyfYcBi8hkybewVQB4H3AkMA+4xFq7wxhzhLX26WIGKJXHBe6tquKq+np2ZEtm12QyXNLTw/l9\nfWjeRHlaG09y1poXuWtFK/sGpu+AYBGZHBNubTbGzAf+CtwOnA/8I1BrjDkIb+roCcUNcfp7NZbk\nhCde4NVY5Y+feCUQ4KyWFi5satqdTLwjGuXhHTv4FyUTIiLTVj7d19cCCeAIa+0svGrJWGvX4FXJ\nvLJo0c0Qg8v1lsF0yTxFHYdr6up4c2srf8yuv7E0leLW9nZu6Ohgfsab/rWlK86LO/pH/NvSpXXl\nRGSw6y2WqdzPw5kqny6P04B3W2tHWx/7RuBfCgtJKs394TCfbWhgU07J7H/r7eVjw0pmd0ZTnHXz\nGkb7nPA78PMPH0pjVV69cMXXuwXfs9eTOfRCqF1Q6mhEZgx1vVWufD69A8D2Mfa5eR5TKtAWv5/P\n1dfzm5yS2SfGYnypq4vlo5TMbqwKcNd5B9EbH7mvNuwvn2QCIJPE6dsKmcrvhhIRmQr5fII/D3wM\nb7rocGfhFb6SaSwB/F9tLd+orSWanQY6N1sy+517KZm9oCG8h70iIlKp8kkorgNuNsYcAdyfPcaH\njTH7A6fjDdKUaeqxUIjLGxp4KVs62++6fKivj0t6eqit4DEgIiJSmHwKW92KV257Lt7Ko2HgFl8X\nLwAAGiZJREFUU8C+wLnW2h8VNUIpC20+Hxc1NvK+WbN2JxNHJRL8uq2Nz3d3T1ky0ej3EZpgt2rI\n8e4nIiKTJ9/CVrcCtxpj6oB6oNNa21fUyKapbck0nemhC95sTHsX47XxJKnU0Ktlo9/HvGDpygyl\ngVurq/lqfT1d2e6NxkyGK7q7Oau/f8qrXM4L+rl7Wcuo5/DTmzu5emEji/3ldQ4rxfJwkIdX7k9N\nbw+kta6KiEzMuBIKY8zlwDestdFhu44BHrHWxooe2TS0LZnmzHXtJMb4Mv/pzZ0jtoUcuHtZS0ku\niH8LBvl0QwPPhAYXLPvHvj4u7+mhOVO6VQDnBf0jzkcg5Z3U5eGgRobnKeJzWFRTRVt/L8mR42ZF\nRPZovF8wvwjUjbL9x8D84oUzvXWmM2MmE2NJuIz4Nj7ZuhyHzzQ08PZZs3YnEwcmk9zT1sZ/dXWV\nNJkQEZHyNN4uj7G+8umr4DTiAndXVfGF+nrackpmX9bTwwf7+mbWfOBwI5llp0O4sdSRiMwo6nqr\nXDPqGiFjezkQ4PKGBh4ND07rfFc0yue7upg3E1skwo24K84sdRQiM4663ipXxSQUxpglwPXA0UAP\ncJe19lOljaryRR2Hb9bW8v9qa0lmVwRdmkrxla4uToyrHLaIiIxPJc2l+wmwCVgKvBk40xjziZJG\nVOFWh8Oc1NrKdXV1JB2HsOtyaXc3D+zYoWRCREQmZLwtFG7231j7JpUx5vXA64CTrbW9QK8x5r+B\ni4BvTvbjl61QHa4viDOB8tCuL8im6mY+19TE6pyS2SdlS2YvG6VkdiUIOw77VUcIOxrWIyJSChMZ\nlPmsMWZ4Z3o18LgxJvcq5Fpri72a0pHAemttd862vwD7G2Nqs0lG0TVUBQj5HRLp8edMIb9Dw1St\nSRFpIXPsNZDoGbq9byv+524gffAFUDM4CSeBww2ty/hG02xiOSWzv9DVxdv3UjK73K2IBHlk1QG0\ntbWRTGr9jXy0JdPctG4b7wg7aCiqiEzUeK98P5zUKPauBegYtm1Xzr5xJRSBwMQu9Iuag/zk/NfR\nGR060nh9e5TP/notX3zbcpa2VA3Z11gVYG796OtVDNRKmKhAIEAwW51yhOBcvKKlg9xAwFulrWEx\nTsMyAP4QDPKftbW8nD0HftflgmiUy/r7vSqXYx2/Qgy8thN9jWXQrkSaa9e/xjH7zaU1VNnvh1LR\n+7BwOoflZ7yvxbhuZa39YEHRTI6BL9Tjvko3NTVN+EFaW0c5zpYuYC1H7DOfQxY0jPtYTT39QNuE\nY2hqaqK1rnrct0/6OtmVvd+ullYuBW7N2X8c8F3H4dDqaqge/3ErQT6v8Uy3IRqnK5XmtbRXn+41\nf4j6SISGgJ8lVVrMLR96HxZO57DyVEoK2AbMGratGS+Z2Dneg3R0dJBKFT6vuaOjb/fx2kKJ8d8v\nOv7bDn28Dtpi469s7nZ5jTk39fdzaVOG7mz3RnMmw+d7ezkrHsdHPqlN+QoEAjQ1NRXtNXbTCejf\nAdWzcfyhvd+hQnWk0pz0wnZy+zIvfH4jAH7goQPm0hRQ2fLxKvb7cCba5cLP+lK8uyZAcyX3w04j\nA+/rvd5uCmIphj8BS4wxzdbaga6OlcAaa23/eA+SSqWK0r8+8EEx0ePl+wHjPc74/7JeBA4CvlNd\nvTuZOKevj091d9PsuqTx1uiYjor1GtO9Af+TV5JeeSXULy38eGWqFrhnWQs9mcyIi2Gdz0etmyGZ\nnIF1SApUtPfhDLQ95XLt+jZWrmilTmX0K0pFJBTW2meMMU8C1xhjLgEWABcDXy9tZOWl03H4an09\nz4R6WZ3ddlAyyTWdnRylDzcZw8KQH/ATDAZpraumLdY3oQRWRAQqJKHIeh/wf8B2oAv4rrX2f0sb\nUnlwgZ9UVfHF+np2+v0cGvMuBh/t7eWd8baKepFFRKQyVcy1xlq7FXhHqeMoNy9lS2Y/llMy+7hk\nhmTNAs6Ip0CD9UVEZApUUqXMshHyOyxrjhDyl65ZuN9x+EpdHae2tu5OJpalUtzR3s5nM3X4jvky\n1Ba7HEj5ejWW5IQnXuDVmLp2RERKoWJaKMrJspYqbjv3wJI9/n2RCJ+tr2dLdm5wxHX5eE8P/9rb\ny0yd5Bd3XV7qjxF369AiuCIiU08JRQXZ6PfzuYYG7o9Edm87OVsye0mFlswWEZHpQQlFBUgA366t\n5Vu1tbtLZs/Llsx+W4WXzBYRyaV1eSqXEooyl2is5V8WzmdTthRywHX5SF8fF/f0UONO+rpsM1fN\nfNJHfxmqRimVKiJFtzmR3l0P5bqDFtPR0cHzMa8eije1WcqdEooylQ4F6F0xn9jc5t2LmKyMx7m6\nq4sDVIFv8vlDM2pQq0gpdaQynLGuPadi62AdXz+wesUsmgKaQ1DulFCUGReILphF7/J5uNmSxw3p\nNFd2d/MP0ai6N0Rk2mkK+PZYsVXJRGVQQlFGknXVdO+/kNTAQmCuS9XWdn6QjLJKTX4iMo2pYmvl\nU0IxxRZv3UxDb8+QbRm/j+jCWcTq0/DyCwAE+qLUbNxBH0HqjzkUr+FvnHq34Hv2ejKHXjhjmu1b\nA34uWTqH1oADaO0JEZGppoQiD+vao1zx6/V86W1LWdZSNe77BXft4rHzTsefGf8FL+Xz8+gTT8H8\nOeMPMJPE6dsKmZlT5Kk16Oey+a20tbVpMSsRkRJQQpGHRNpl3a4YifTEZlkkm5s55uZ7h7RQuEDH\n4StwgwHC7d1Ub9qBLzlYU6Krto6rm5uLFbqIiMikUEIxxTbOXzhiWzoVwHV9BAI+WNZYgqhEREQK\no6GzZcCfSBGIJkodhuSKd+K8ejfEO0sdiYhIRVBCITKaeCe+dfcqoRARGSclFCIiIlIwJRQiIiJS\nMCUU01G4kcyy0yE8cwZ4xjIuL/RFiWW0vomISCkoochDS02Q81fOpaUmWOpQRhduxF1x5oxKKNbG\nk5z05Iusjc+c2hsiIuVE00bzMKsmyIePnlfqMERERMqGWihERESkYGqhEMnVvwNS/RDdgRuZBdEd\n3vZANVTPLm1sIiJlTAmFyIBED75H/xOHwYGd/mf/BwDX8ZE5/lsQqitVdCIiZU0JhciAUB2ZY7/q\ntVAMF6hWMiEisgdKKERyqVtDRCQvGpQ5HaUT0LvF+ykiIjIFlFDkIZ7KsLY9SjyVKXUoo+vbiv/x\nz0Df1lJHMuk2J9I8H0uSyLjccPASEhmX52NJNifSe7+ziIgUjbo88rB+V4wP3vkiPzh7f/afXV3q\ncGasjlSGM9a1M5jW7dz9Pz+wesUsmgLKmUVEpoISCqlYTQEf9yxroSeTIRAI0NTUREdHB6lUijqf\nT8mEiMgUUkIxnQzUUBjo6hj4OY1rKCwM+QE/wWCQ1rpq2mJ9JJNOqcMSEZlxlFBMF6PVUHjuBkA1\nFEREZPIpoZguVENBRERKSAnFdDJNuzVERKT8adTaFGr0+whNsHs/5Hj3ExERKWdqoZhC84J+7l7W\nQmd6aP2KjWmXT2/u5OqFjSz2D804Gv0+5gX9UxmmiIjIhCmhyMPS5gi3nnMACxrCE77vvKB/RIIQ\nSHkDKZeHg+wb0AwFERGpPEoo8hAO+FjeUlXqMERERMqGOudFRESkYGqhmIAtXXF64yPXiKgN+/Pq\n/hAREZkulFCMU2c0xVk3ryHjjtznd+DnHz6UxiqdThERmZl0BRynxqoAd5130JgtFEomRERkJquY\nq6Ax5vXAHUCbtfbYUsQwWd0aYcdhv+oIYUczPEREpDJVxKBMY8z7gZ8AL5U6lsmwIhLkkVUHsCIS\nLHUoIiIieamIhAIIA6uAJ0sdiIiIiIxUEV0e1tofABhjSh2KiIiIjKJSWihERESkjJVFC4Ux5hzg\nFiB3UqaT/f2D1tqbi/E4gUBZPN0RBuIq1/gqgc5h4XQOC6dzWDidw/Iz3teiLF4xa+1twG2T/ThN\nTU2T/RAFKff4KoHOYeF0Dgunc1g4ncPKUxYJxVTp6OgglUqVOowRAoEATU1NZRtfJdA5LJzOYeF0\nDgunc1h+Bl6Tvd5uCmIppoIKNaRSKZLJZLFiKbpyj68S6BwWTuewcDqHhdM5rDwVkVAYY14AFuPF\n6zPGRPHGV+xvrd1U0uCK4NVYkvc+8QJfm1/PYv/eby8iIlJuKiKhsNYeUOoYJlPcdXmpP0bcraPA\nRhgREZGS0LRRERERKZgSChERESmYEgoREREpmBIKERERKZgSChERESmYEgoREREpmBKKMtAa8HPJ\n0jm0BlSEQkREKpMSijLQGvRz2bJ5tAaVUIiISGVSQiEiIiIFU0IhIiIiBVNCISIiIgVTQiEiIiIF\nU0IhIiIiBauI1Uanq82JND2ZDIGUS1NPPx3RBKlUijqfj4UhzfgQEZHKoYSiRDpSGc5Y105m95a2\n3f/zA6tXzKIpoAYkERGpDEooSqQp4OOeZS1eC0UgQFNTEx0dHbtbKJRMiIhIJVFCUUJet4afYDBI\na101bbE+kkmn1GGJiIhMmL4Gi4iISMGUUIiIiEjBlFCIiIhIwZRQiIiISMGUUIiIiEjBlFCIiIhI\nwZRQiIiISMGUUIiIiEjBlFCIiIhIwZRQiIiISMGUUIiIiEjBlFCIiIhIwZRQiIiISMGUUIiIiEjB\nlFCIiIhIwZRQiIiISMGUUIiIiEjBlFCIiIhIwZRQiIiISMGUUIiIiEjBlFCIiIhIwZRQiIiISMGU\nUIiIiEjBlFCIiIhIwQKlDmA8jDHNwDeA0/BifgS4yFq7uaSBiYiICFA5LRQ3Aa3AQcB+QBj4fikD\nEhERkUEV0UIBbAKut9Z2ABhjvgv8qLQhiYiIyICKSCistRcO27QY2FaKWERERGSkikgochljlgJf\nAC6bwN0iAOFwmECg/J6y3+8Hyje+SqBzWDidw8LpHBZO57D8DLwmZK+lYymLV8sYcw5wC+DmbHay\nv3/QWntz9nYHAPcBP7DW3jSBh1gKUF9fX4xwJ025x1cJdA4Lp3NYOJ3DwukclqWlwKNj7XRc1x1r\nX1kxxqwEfgl83Vr7tQnevQV4C7AeiBU5NBERkeksgpdM3Ae0j3WjikgojDH7An8ELrHW3lLqeERE\nRGSoSpk2ej1wg5IJERGR8lT2LRTGmIXABiCR3eQyOL7iNGvtH0oVm4iIiHjKPqEQERGR8lcpXR4i\nIiJSxpRQiIiISMGUUIiIiEjBlFCIiIhIwZRQiIiISMGUUIiIiEjBymItj5nMGLMY+CZwAl6tjfuA\ni6y13SUNrAIZY76Bd+6UKOfBGPMZ4EKgDngM+Ii1dkNpo6oMxpjDgP8GjgSiwAPAxdbanSUNrMwZ\nY94C/BB40Fr7/mH7zgIuB5YBLwKXW2vvn/ooZbz0wVt6Pwd2AYuA1wMHA/9V0ogqkDHmcOCfGLrA\nnIyTMeZC4P14ie08YA1wcUmDqhDGGB/wK7xFk1rx/oZn41X4lTEYYy7D+zL10ij7DgduAv4DmAV8\nA7jbGDN/KmOUiVELRQkZYxqAp4BPW2ujQNQY80Pg46WNrLIYYxzgu8C1wJdKHE6l+iTwSWvtK9nf\nP1HKYCrMfLwk7FZrbQroMMb8FLiktGGVvSiwEvg2EB6270PAL62192V/v90Y83HgXGCii0PKFFFC\nUULW2i7gw8M2Lwa2lCCcSvZRvA+n21FCMWHZb33LgBZjzHPAHOAh4F/VZD8uW4CngQuMMZ8DaoD3\n4bU+yhistdcBGGNG230U8Ith2/4CvGGSw5ICqMujjBhjXg/8G7oojpsxZg5wJfCvJQ6lki3M/nwf\ncDLwuuy2G0oWUQWx1rp45+4MoBvYhvfZenkp46pwLUDHsG278Lo/pEwpoSgTxpjj8AZk/oe19qFS\nx1NBrgVutNa+WOpAKpiT/flVa+1r1tqtwOeBdxtjQiWMqyJkz9HPgbuABmABXmJxeynjmoYGFoWU\nMqUujzJgjHkncCtwobX2tlLHUymMMacAxwIfyW5y9nBzGdv27M+unG3r8c7nbGDzVAdUYU4Bllpr\nB1okeo0xnweeMcY0Wms7SxhbpWpjZGtEc3a7lCm1UJSYMeZYvGlT71UyMWHn4F3wNhpj2oA/A44x\nZocZo2NWRrUZ7xv14TnblgFJYGtJIqosfsCXne0xIIK+TRfiT3jjKHK9AXiiBLHIOKmFooSMMX7g\n/4D/tNY+UOp4KtDFwBU5vy/Cq59wGCP7X2UM1tq0MeZG4DPGmN8DPcBngVustZnSRlcRHgV6gauM\nMV8BqvHGT/xOrRN5+z/gSWPM24AH8b487IvXkitlynFdJdGlYox5I/A7IM5g/+DAz/2ttZtKGF7F\nMcYsAdZaa/2ljqXSZMcBXItXiyIA/Bj4uLW2v6SBVQhjzBF45+8wvL/nh/Gm4W7f0/1mMmNMFO+z\nLpjdlAJca211dv8ZwFfxZr6tAf7dWvvHUsQq46OEQkRERAqmMRQiIiJSMCUUIiIiUjAlFCIiIlIw\nJRQiIiJSMCUUIiIiUjAlFCIiIlIwJRQiIiJSMCUUIiIiUjAlFCIiIlIwreUhUgLGmJuAt1hr55U6\nlgHGmB/gxTR/Eo79UeB/8Fbl3DhZj2WMORF4CHirtXb1KPtXA/tZa5fu4Rh/AgLW2sPHus2w238Y\nuAHYx1q7Nq/ARaYBtVCIlIZL+a1G+e/AoQO/GGM+aIx5qEjHHv58hzxWke3pvH4PWGSMOXm0ncaY\ng4Ej8RanmsjjldtrKTLl1EIhIgBYa3uGbTqGSbpQjvJYxeTsYd89eCvRfgBvFcvhPgDEgNuKHpXI\nNKeEQqSMGWPeCXwGbxXLDPAX4KqB5e5zmvjfBFwAvA1IA78E/tVaG83ebj7wv8DJQB/wQ+DvwE0M\ndkPcBJxmrZ2fbZk4MXvfNPBBYAOjdCcYYx4GQtbaY7O/z8PrAjgZ6AfuBF4Z9rxuIqfLxxizDu9i\n/xJwKTAHeA74N2vtUzn3+wTwEWAfvGXW/wRcZq19djzn01qbMMbcCnzIGFNrre3NObYPb7XVnwws\nO26M8QNXZbcvBNqBR4BLx1oNOHv8E621i3K2rQBeBj5srf1+dtv+wDXAG4BmvNfjSmvtr8bzXETK\njbo8RMqUMebNwL3A08DrgVXAVuDXxpjh/fvXAvfjNdd/CjgP+HjO/h8DK4F/wEsUGoArGNoCkfv/\n9+AlL48Cc4G7RrnNaPcje9ujgDOBY4HtwGWj3Gf4/d6SfY7vBE7Cu8j+cGCnMeafss/z28Dy7G3S\nwC+NMeFR4hrL94AawAzb/la853pjzrbPAZcAFwPLgHcBKwC7h+PvtQvEGDML+D2wAHgfcATwGHCv\nMeaN430iIuVELRQi5esyYI219mMDG7IX1TcDH8NrkRjwgLX2puz/bzTGXI6XQGCM2Qc4GrjQWvvr\n7G3+xRjzFGOw1nYYY5JAwlrblj0O7Lk7YeCx3gh8NKcV48vGmJV4icKe1ON9g09lj3UzcGVOS8K9\nwKHW2jXZ228xxnwHrzXmEODPezn+wHP7e/a5fwD4fs6uDwBrrbUP52z7NnCLtXaghWVLdkDpt40x\njQMtGXn4F6AJONJauzm77SJjzEl4CeHezpVI2VFCIVK+Xs+wb8LW2mR2FsKRw277xLDf2/AuWOB9\no3bxugdy/XKU4xTq4Oxj/WXY9kfZ+0XyLwPJRFZb9mcT0IvXffIOY8wPgSVAFeDP3qZlgnHeCHzX\nGLPcWrvWGNOI1/pw1bDbxYEPGmPeDcwHQgx+bjYD+SYUK4GXcpKJAQ8xsuVEpCIooRApX/VA1yjb\nu/Ga33P1DvvdZbA1YeBiO3wgZBvFVzdGPOMZhDnac4DB53Et8G94F/2f4Z2Ho4FbJh4mdwD/jdcq\n8TngH/GSkx8Ou91deF0rl+GNnegHzgK+lMdj5qoH9jfGDD8vAcBvjHGstZo5IhVFCYVI+erEG+sw\nXAMT+2Ycz/6sHrZ91gTjGX6BH1ALJLP/7xvjsRon+FijOQe4w1r7hYENxphV+RzIWttjjPkRcC5e\nQvFPwG+stdtyjt2EN8j1C9ba/8nZvrfPzdxkbkDtsN878AagvmOU26JkQiqRBmWKlK8n8MYj7GaM\nieB1hTyZs3lvF5+X8S5aK4dtf884Ysi92A0kMa058TQCB+Tc5vnsfYZf6E8Yx2PtTQjYOWzbP2d/\n5sY53ovxjcASY8wZeC0dw2tPBLM/dz9mNpl4/yiPmasTaDbG5O4fPgX3cWAx0GWtXTvwD2+Q6Wvj\njF+krKiFQqR0fMaYOaNsj1lru4CvAQ8YY67HGxxYBVwJhIHv5Nx+jwMlrbV/M8a8AHzKGPMSsAVv\n1sLevlB0AIcZY47Cu8i9kt12oTHmabwL7hfxZnEMPNYLxpg/A582xrwIbMbrTjhwL481Ho8B7zHG\n3I7XhfIfwKvAqcBxxpjHs7fb4/nIifUP2fNxffY5/HLY/h3GmLV4YygewktovoI3zmE/4ERjzGgX\n/yeBC4HLjTG34c3gOHfYbW4EPgH8yBhzBd7snWPwXtcb8QZmilQUtVCIlE4r3oVk+L8fAFhrHwHe\njTcF8y94ffjVwEnW2pdyjjPWN/Lc7WcA64Bf4E0v3YRXlwK8Qk6j3ee/8S7OvwfeZ63tx+t2qAee\nwhtfcHv2/7neB6wBfg78EZgNfHYv8Y2n2uTH8JKaB4DfAC9Yay8Efoo3xuGiUY67N9/Hmyp6k7U2\nM8r+f8we70m8ehp3ZB/nCeA6vKmxw92Ol6R8HHgWbzZO7owcrLXtwPF4Cdov8Lo/vgB8zVqrZEIq\nkuO66qoTme6MMVVAOHeaozHmm8CHrLV1Y99TRGR81OUhMjOsBuZmF7Jah1dw6kN4FS1FRAqmhEJk\nZngP8HW85vhGvC6P/wKuLmVQIjJ9qMtDRERECqZBmSIiIlIwJRQiIiJSMCUUIiIiUjAlFCIiIlIw\nJRQiIiJSMCUUIiIiUjAlFCIiIlIwJRQiIiJSsP8PRUQRwIOWmowAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fccc7390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_signf_progs(time_dfs, result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate Progression Scores; Folds Ratio Normalization\n",
    "\n",
    "Now the raw values will be turned into fold ratios with respect to baseline (longitudinal value 0). Any other longitudinal value could be chosen as reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "progcol = datacol.create_progression_tables(feats, \"TIME\", \"ENTITY\", \"robl\", 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyze Progression Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>p-value</th>\n",
       "      <th>cor_p-value</th>\n",
       "      <th>signf</th>\n",
       "      <th>Dataset1conf.</th>\n",
       "      <th>Dataset2conf.</th>\n",
       "      <th>Dataset3conf.</th>\n",
       "      <th>Dataset1</th>\n",
       "      <th>Dataset2</th>\n",
       "      <th>Dataset3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>features</th>\n",
       "      <th></th>\n",
       "      <th>datasets</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">FEAT1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>4.975266e-01</td>\n",
       "      <td>9.716205e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.15, -0.04]</td>\n",
       "      <td>[-0.17, -0.04]</td>\n",
       "      <td>[-0.13, -0.03]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>3.903372e-01</td>\n",
       "      <td>9.686940e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.15, -0.04]</td>\n",
       "      <td>[-0.17, -0.04]</td>\n",
       "      <td>[-0.13, -0.03]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>6.024019e-01</td>\n",
       "      <td>9.716205e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.15, -0.04]</td>\n",
       "      <td>[-0.17, -0.04]</td>\n",
       "      <td>[-0.13, -0.03]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.682777e-01</td>\n",
       "      <td>7.478967e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>[-0.29, -0.16]</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.602198e-01</td>\n",
       "      <td>9.602198e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>[-0.29, -0.16]</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>2.653063e-01</td>\n",
       "      <td>7.086431e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>[-0.29, -0.16]</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.224886e-01</td>\n",
       "      <td>5.004763e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.38, -0.23]</td>\n",
       "      <td>[-0.3, -0.14]</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>3.871942e-02</td>\n",
       "      <td>2.584176e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.38, -0.23]</td>\n",
       "      <td>[-0.3, -0.14]</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>6.615197e-01</td>\n",
       "      <td>8.854311e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.38, -0.23]</td>\n",
       "      <td>[-0.3, -0.14]</td>\n",
       "      <td>[-0.25, -0.12]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.505636e-05</td>\n",
       "      <td>6.002931e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>[-0.5, -0.36]</td>\n",
       "      <td>[-0.29, -0.16]</td>\n",
       "      <td>[-0.18, -0.04]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.630613e-07</td>\n",
       "      <td>1.467551e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>[-0.5, -0.36]</td>\n",
       "      <td>[-0.29, -0.16]</td>\n",
       "      <td>[-0.18, -0.04]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.365632e-02</td>\n",
       "      <td>9.176581e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.5, -0.36]</td>\n",
       "      <td>[-0.29, -0.16]</td>\n",
       "      <td>[-0.18, -0.04]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>4.267387e-07</td>\n",
       "      <td>2.560430e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>[-0.65, -0.47]</td>\n",
       "      <td>[-0.32, -0.17]</td>\n",
       "      <td>[-0.12, 0.02]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>2.611855e-11</td>\n",
       "      <td>2.350670e-10</td>\n",
       "      <td>True</td>\n",
       "      <td>[-0.65, -0.47]</td>\n",
       "      <td>[-0.32, -0.17]</td>\n",
       "      <td>[-0.12, 0.02]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>5.112514e-04</td>\n",
       "      <td>2.553645e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[-0.65, -0.47]</td>\n",
       "      <td>[-0.32, -0.17]</td>\n",
       "      <td>[-0.12, 0.02]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">LONG1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>4.750820e-06</td>\n",
       "      <td>3.800592e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.12, 0.29]</td>\n",
       "      <td>[-0.17, -0.01]</td>\n",
       "      <td>[0.11, 0.29]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>9.073517e-01</td>\n",
       "      <td>9.716205e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.12, 0.29]</td>\n",
       "      <td>[-0.17, -0.01]</td>\n",
       "      <td>[0.11, 0.29]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>3.701472e-06</td>\n",
       "      <td>3.331275e-05</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.12, 0.29]</td>\n",
       "      <td>[-0.17, -0.01]</td>\n",
       "      <td>[0.11, 0.29]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>1.395546e-05</td>\n",
       "      <td>1.255921e-04</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.36, 0.5]</td>\n",
       "      <td>[0.15, 0.28]</td>\n",
       "      <td>[0.26, 0.43]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.608860e-01</td>\n",
       "      <td>5.839891e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.36, 0.5]</td>\n",
       "      <td>[0.15, 0.28]</td>\n",
       "      <td>[0.26, 0.43]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.497889e-02</td>\n",
       "      <td>8.657427e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.36, 0.5]</td>\n",
       "      <td>[0.15, 0.28]</td>\n",
       "      <td>[0.26, 0.43]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>5.612767e-04</td>\n",
       "      <td>5.040164e-03</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.51, 0.65]</td>\n",
       "      <td>[0.33, 0.47]</td>\n",
       "      <td>[0.44, 0.6]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>2.467601e-01</td>\n",
       "      <td>6.780909e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.51, 0.65]</td>\n",
       "      <td>[0.33, 0.47]</td>\n",
       "      <td>[0.44, 0.6]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>3.668144e-02</td>\n",
       "      <td>2.584176e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.51, 0.65]</td>\n",
       "      <td>[0.33, 0.47]</td>\n",
       "      <td>[0.44, 0.6]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>6.823305e-02</td>\n",
       "      <td>3.455992e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.62, 0.78]</td>\n",
       "      <td>[0.55, 0.68]</td>\n",
       "      <td>[0.55, 0.71]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.745390e-01</td>\n",
       "      <td>5.357133e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.62, 0.78]</td>\n",
       "      <td>[0.55, 0.68]</td>\n",
       "      <td>[0.55, 0.71]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>5.167256e-01</td>\n",
       "      <td>7.664459e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.62, 0.78]</td>\n",
       "      <td>[0.55, 0.68]</td>\n",
       "      <td>[0.55, 0.71]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>5.937478e-10</td>\n",
       "      <td>4.749983e-09</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.75, 0.89]</td>\n",
       "      <td>[0.33, 0.49]</td>\n",
       "      <td>[0.66, 0.81]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.360224e-01</td>\n",
       "      <td>4.428015e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[0.75, 0.89]</td>\n",
       "      <td>[0.33, 0.49]</td>\n",
       "      <td>[0.66, 0.81]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>1.952189e-07</td>\n",
       "      <td>1.366532e-06</td>\n",
       "      <td>True</td>\n",
       "      <td>[0.75, 0.89]</td>\n",
       "      <td>[0.33, 0.49]</td>\n",
       "      <td>[0.66, 0.81]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"15\" valign=\"top\">NDSIG1</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.254222e-01</td>\n",
       "      <td>9.716205e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.17, 0.14]</td>\n",
       "      <td>[-0.21, 0.1]</td>\n",
       "      <td>[-0.06, 0.05]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>5.832477e-01</td>\n",
       "      <td>9.716205e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.17, 0.14]</td>\n",
       "      <td>[-0.21, 0.1]</td>\n",
       "      <td>[-0.06, 0.05]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>4.477097e-01</td>\n",
       "      <td>9.716205e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.17, 0.14]</td>\n",
       "      <td>[-0.21, 0.1]</td>\n",
       "      <td>[-0.06, 0.05]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>7.365131e-03</td>\n",
       "      <td>5.043065e-02</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.46, -0.04]</td>\n",
       "      <td>[-0.11, 0.18]</td>\n",
       "      <td>[-0.03, 0.09]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>5.494996e-03</td>\n",
       "      <td>4.312374e-02</td>\n",
       "      <td>True</td>\n",
       "      <td>[-0.46, -0.04]</td>\n",
       "      <td>[-0.11, 0.18]</td>\n",
       "      <td>[-0.03, 0.09]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>5.239182e-01</td>\n",
       "      <td>7.733462e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.46, -0.04]</td>\n",
       "      <td>[-0.11, 0.18]</td>\n",
       "      <td>[-0.03, 0.09]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">6</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>3.009402e-01</td>\n",
       "      <td>6.780909e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.22, 0.07]</td>\n",
       "      <td>[-0.13, 0.18]</td>\n",
       "      <td>[-0.02, 0.08]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>1.092428e-01</td>\n",
       "      <td>5.004763e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.22, 0.07]</td>\n",
       "      <td>[-0.13, 0.18]</td>\n",
       "      <td>[-0.02, 0.08]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>9.955782e-01</td>\n",
       "      <td>9.955782e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.22, 0.07]</td>\n",
       "      <td>[-0.13, 0.18]</td>\n",
       "      <td>[-0.02, 0.08]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">8</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>2.964661e-01</td>\n",
       "      <td>6.517789e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.25, 0.08]</td>\n",
       "      <td>[-0.13, 0.09]</td>\n",
       "      <td>[-0.03, 0.08]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>6.995567e-02</td>\n",
       "      <td>3.455992e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.25, 0.08]</td>\n",
       "      <td>[-0.13, 0.09]</td>\n",
       "      <td>[-0.03, 0.08]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>5.167256e-01</td>\n",
       "      <td>7.664459e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.25, 0.08]</td>\n",
       "      <td>[-0.13, 0.09]</td>\n",
       "      <td>[-0.03, 0.08]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">10</th>\n",
       "      <th>(1, 2)</th>\n",
       "      <td>6.615139e-01</td>\n",
       "      <td>8.969451e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.15, 0.13]</td>\n",
       "      <td>[-0.1, 0.14]</td>\n",
       "      <td>[-0.04, 0.05]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(1, 3)</th>\n",
       "      <td>5.311618e-01</td>\n",
       "      <td>8.969451e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.15, 0.13]</td>\n",
       "      <td>[-0.1, 0.14]</td>\n",
       "      <td>[-0.04, 0.05]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(2, 3)</th>\n",
       "      <td>6.615246e-01</td>\n",
       "      <td>8.969451e-01</td>\n",
       "      <td>False</td>\n",
       "      <td>[-0.15, 0.13]</td>\n",
       "      <td>[-0.1, 0.14]</td>\n",
       "      <td>[-0.04, 0.05]</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           p-value   cor_p-value  signf   Dataset1conf.  \\\n",
       "features    datasets                                                      \n",
       "FEAT1    2  (1, 2)    4.975266e-01  9.716205e-01  False  [-0.15, -0.04]   \n",
       "            (1, 3)    3.903372e-01  9.686940e-01  False  [-0.15, -0.04]   \n",
       "            (2, 3)    6.024019e-01  9.716205e-01  False  [-0.15, -0.04]   \n",
       "         4  (1, 2)    3.682777e-01  7.478967e-01  False  [-0.25, -0.12]   \n",
       "            (1, 3)    9.602198e-01  9.602198e-01  False  [-0.25, -0.12]   \n",
       "            (2, 3)    2.653063e-01  7.086431e-01  False  [-0.25, -0.12]   \n",
       "         6  (1, 2)    1.224886e-01  5.004763e-01  False  [-0.38, -0.23]   \n",
       "            (1, 3)    3.871942e-02  2.584176e-01  False  [-0.38, -0.23]   \n",
       "            (2, 3)    6.615197e-01  8.854311e-01  False  [-0.38, -0.23]   \n",
       "         8  (1, 2)    7.505636e-05  6.002931e-04   True   [-0.5, -0.36]   \n",
       "            (1, 3)    1.630613e-07  1.467551e-06   True   [-0.5, -0.36]   \n",
       "            (2, 3)    1.365632e-02  9.176581e-02  False   [-0.5, -0.36]   \n",
       "         10 (1, 2)    4.267387e-07  2.560430e-06   True  [-0.65, -0.47]   \n",
       "            (1, 3)    2.611855e-11  2.350670e-10   True  [-0.65, -0.47]   \n",
       "            (2, 3)    5.112514e-04  2.553645e-03   True  [-0.65, -0.47]   \n",
       "LONG1    2  (1, 2)    4.750820e-06  3.800592e-05   True    [0.12, 0.29]   \n",
       "            (1, 3)    9.073517e-01  9.716205e-01  False    [0.12, 0.29]   \n",
       "            (2, 3)    3.701472e-06  3.331275e-05   True    [0.12, 0.29]   \n",
       "         4  (1, 2)    1.395546e-05  1.255921e-04   True     [0.36, 0.5]   \n",
       "            (1, 3)    1.608860e-01  5.839891e-01  False     [0.36, 0.5]   \n",
       "            (2, 3)    1.497889e-02  8.657427e-02  False     [0.36, 0.5]   \n",
       "         6  (1, 2)    5.612767e-04  5.040164e-03   True    [0.51, 0.65]   \n",
       "            (1, 3)    2.467601e-01  6.780909e-01  False    [0.51, 0.65]   \n",
       "            (2, 3)    3.668144e-02  2.584176e-01  False    [0.51, 0.65]   \n",
       "         8  (1, 2)    6.823305e-02  3.455992e-01  False    [0.62, 0.78]   \n",
       "            (1, 3)    1.745390e-01  5.357133e-01  False    [0.62, 0.78]   \n",
       "            (2, 3)    5.167256e-01  7.664459e-01  False    [0.62, 0.78]   \n",
       "         10 (1, 2)    5.937478e-10  4.749983e-09   True    [0.75, 0.89]   \n",
       "            (1, 3)    1.360224e-01  4.428015e-01  False    [0.75, 0.89]   \n",
       "            (2, 3)    1.952189e-07  1.366532e-06   True    [0.75, 0.89]   \n",
       "NDSIG1   2  (1, 2)    7.254222e-01  9.716205e-01  False   [-0.17, 0.14]   \n",
       "            (1, 3)    5.832477e-01  9.716205e-01  False   [-0.17, 0.14]   \n",
       "            (2, 3)    4.477097e-01  9.716205e-01  False   [-0.17, 0.14]   \n",
       "         4  (1, 2)    7.365131e-03  5.043065e-02  False  [-0.46, -0.04]   \n",
       "            (1, 3)    5.494996e-03  4.312374e-02   True  [-0.46, -0.04]   \n",
       "            (2, 3)    5.239182e-01  7.733462e-01  False  [-0.46, -0.04]   \n",
       "         6  (1, 2)    3.009402e-01  6.780909e-01  False   [-0.22, 0.07]   \n",
       "            (1, 3)    1.092428e-01  5.004763e-01  False   [-0.22, 0.07]   \n",
       "            (2, 3)    9.955782e-01  9.955782e-01  False   [-0.22, 0.07]   \n",
       "         8  (1, 2)    2.964661e-01  6.517789e-01  False   [-0.25, 0.08]   \n",
       "            (1, 3)    6.995567e-02  3.455992e-01  False   [-0.25, 0.08]   \n",
       "            (2, 3)    5.167256e-01  7.664459e-01  False   [-0.25, 0.08]   \n",
       "         10 (1, 2)    6.615139e-01  8.969451e-01  False   [-0.15, 0.13]   \n",
       "            (1, 3)    5.311618e-01  8.969451e-01  False   [-0.15, 0.13]   \n",
       "            (2, 3)    6.615246e-01  8.969451e-01  False   [-0.15, 0.13]   \n",
       "\n",
       "                       Dataset2conf.   Dataset3conf.  Dataset1  Dataset2  \\\n",
       "features    datasets                                                       \n",
       "FEAT1    2  (1, 2)    [-0.17, -0.04]  [-0.13, -0.03]        40        40   \n",
       "            (1, 3)    [-0.17, -0.04]  [-0.13, -0.03]        40        40   \n",
       "            (2, 3)    [-0.17, -0.04]  [-0.13, -0.03]        40        40   \n",
       "         4  (1, 2)    [-0.29, -0.16]  [-0.25, -0.12]        40        40   \n",
       "            (1, 3)    [-0.29, -0.16]  [-0.25, -0.12]        40        40   \n",
       "            (2, 3)    [-0.29, -0.16]  [-0.25, -0.12]        40        40   \n",
       "         6  (1, 2)     [-0.3, -0.14]  [-0.25, -0.12]        40        40   \n",
       "            (1, 3)     [-0.3, -0.14]  [-0.25, -0.12]        40        40   \n",
       "            (2, 3)     [-0.3, -0.14]  [-0.25, -0.12]        40        40   \n",
       "         8  (1, 2)    [-0.29, -0.16]  [-0.18, -0.04]        40        40   \n",
       "            (1, 3)    [-0.29, -0.16]  [-0.18, -0.04]        40        40   \n",
       "            (2, 3)    [-0.29, -0.16]  [-0.18, -0.04]        40        40   \n",
       "         10 (1, 2)    [-0.32, -0.17]   [-0.12, 0.02]        40        40   \n",
       "            (1, 3)    [-0.32, -0.17]   [-0.12, 0.02]        40        40   \n",
       "            (2, 3)    [-0.32, -0.17]   [-0.12, 0.02]        40        40   \n",
       "LONG1    2  (1, 2)    [-0.17, -0.01]    [0.11, 0.29]        40        40   \n",
       "            (1, 3)    [-0.17, -0.01]    [0.11, 0.29]        40        40   \n",
       "            (2, 3)    [-0.17, -0.01]    [0.11, 0.29]        40        40   \n",
       "         4  (1, 2)      [0.15, 0.28]    [0.26, 0.43]        40        40   \n",
       "            (1, 3)      [0.15, 0.28]    [0.26, 0.43]        40        40   \n",
       "            (2, 3)      [0.15, 0.28]    [0.26, 0.43]        40        40   \n",
       "         6  (1, 2)      [0.33, 0.47]     [0.44, 0.6]        40        40   \n",
       "            (1, 3)      [0.33, 0.47]     [0.44, 0.6]        40        40   \n",
       "            (2, 3)      [0.33, 0.47]     [0.44, 0.6]        40        40   \n",
       "         8  (1, 2)      [0.55, 0.68]    [0.55, 0.71]        40        40   \n",
       "            (1, 3)      [0.55, 0.68]    [0.55, 0.71]        40        40   \n",
       "            (2, 3)      [0.55, 0.68]    [0.55, 0.71]        40        40   \n",
       "         10 (1, 2)      [0.33, 0.49]    [0.66, 0.81]        40        40   \n",
       "            (1, 3)      [0.33, 0.49]    [0.66, 0.81]        40        40   \n",
       "            (2, 3)      [0.33, 0.49]    [0.66, 0.81]        40        40   \n",
       "NDSIG1   2  (1, 2)      [-0.21, 0.1]   [-0.06, 0.05]        40        40   \n",
       "            (1, 3)      [-0.21, 0.1]   [-0.06, 0.05]        40        40   \n",
       "            (2, 3)      [-0.21, 0.1]   [-0.06, 0.05]        40        40   \n",
       "         4  (1, 2)     [-0.11, 0.18]   [-0.03, 0.09]        40        40   \n",
       "            (1, 3)     [-0.11, 0.18]   [-0.03, 0.09]        40        40   \n",
       "            (2, 3)     [-0.11, 0.18]   [-0.03, 0.09]        40        40   \n",
       "         6  (1, 2)     [-0.13, 0.18]   [-0.02, 0.08]        40        40   \n",
       "            (1, 3)     [-0.13, 0.18]   [-0.02, 0.08]        40        40   \n",
       "            (2, 3)     [-0.13, 0.18]   [-0.02, 0.08]        40        40   \n",
       "         8  (1, 2)     [-0.13, 0.09]   [-0.03, 0.08]        40        40   \n",
       "            (1, 3)     [-0.13, 0.09]   [-0.03, 0.08]        40        40   \n",
       "            (2, 3)     [-0.13, 0.09]   [-0.03, 0.08]        40        40   \n",
       "         10 (1, 2)      [-0.1, 0.14]   [-0.04, 0.05]        40        40   \n",
       "            (1, 3)      [-0.1, 0.14]   [-0.04, 0.05]        40        40   \n",
       "            (2, 3)      [-0.1, 0.14]   [-0.04, 0.05]        40        40   \n",
       "\n",
       "                      Dataset3  \n",
       "features    datasets            \n",
       "FEAT1    2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "LONG1    2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "NDSIG1   2  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         4  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         6  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         8  (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  \n",
       "         10 (1, 2)          33  \n",
       "            (1, 3)          33  \n",
       "            (2, 3)          33  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result, time_dfs = progcol.analyze_longitudinal_feats(\"TIME\", 0, include=feats)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAGNCAYAAAAo48qxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd0915f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAGNCAYAAAAo48qxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxO7/A/vRsJZRiiKZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fce2c438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAiAAAAGNCAYAAAAo48qxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlN3/B9TQQBMUMPmkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa0fd0305f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dc.plot_signf_progs(time_dfs, result)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}